%%%%%%%%%%%%%%%%%%% 2026 %%%%%%%%%%%%%%%%%%% 
@article{Maddalena2026SciData,
author = {Maddalena, Lucia and Yildiz, Beril and Blanco, Francesca Del Vecchio and Guarracino, Mario Rosario},
title = {A Curated Dataset for Chemical Characterization of Heated Tobacco Product Aerosol},
journal = {Scientific Data},
volume = {},
number = {},
pages = {},
doi = {https://doi.org/10.1038/s41597-026-08055-9},
year = {2026}
}

@dataset{Maddalena2026HTP-AeroChem, 
author = {Maddalena, Lucia and Yildiz, Beril and Blanco, Francesca Del Vecchio and Guarracino, Mario Rosario},
title ={HTP-AeroChem Dataset}, 
year = "2026",
month = "2",
url = "https://figshare.com/articles/dataset/HTP-AeroChem_dataset/31347187",
doi = "10.6084/m9.figshare.31347187.v2",
} 

@article{Maddalena2026RiskAnalysis,
author = {Maddalena, Lucia and Yildiz, Beril and Blanco, Francesca Del Vecchio and Guarracino, Mario Rosario},
title = {Risk Assessment Models for Heated Tobacco Products},
journal = {Risk Analysis},
volume = {46},
number = {8},
pages = {e70317},
keywords = {cancer risk, heated tobacco product, mathematical modeling, noncancer hazard, risk assessment},
doi = {https://doi.org/10.1111/risa.70317},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/risa.70317},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/risa.70317},
abstract = {ABSTRACT Heated tobacco products (HTPs) are marketed as alternatives to conventional cigarettes with a potential reduced risk profile. Yet, their actual impact on cancer and noncancer disease risk remains uncertain and requires rigorous quantitative assessment. In this study, we develop a unified and transparent computational framework for toxicological risk assessment of HTPs, integrating chemical emissions data with compound-specific toxicological thresholds derived from regulatory agencies. Our work (i) systematically reviews and harmonizes existing risk models used in the literature, (ii) formulates generalizable mathematical models for estimating lifetime cancer risk, hazard quotients, and margins of exposure that account for population demographics, smoking habits, and compound characteristics, and (iii) validates these models by reproducing published results and exploring the sensitivity of risk estimates to model parameters and emission sources. Using emissions data from conventional cigarettes and HTPs, we quantify per-compound and aggregated cancer and noncancer risks, and evaluate the relative risk reduction associated with switching from cigarettes to HTPs. The proposed risk analysis models provide a reproducible, extensible, and transparent approach for computational toxicology assessment, and can be readily applied to emerging nicotine and tobacco products within harm-reduction evaluation paradigms.},
year = {2026}
}


%%%%%%%%%%%%%%%%%%% 2025 %%%%%%%%%%%%%%%%%%% 
@Inbook{Maddalena2025BIOMAT,
	author="Maddalena, L.  and Antonelli, L.  and Polverino, F.  and Asteriti, I. A.  and Degrassi, F.  and Guarguaglini, G.  and Albu, A.  and Hada, A.  and Guarracino, M. R.",
	editor="Mondaini, Rubem P.",
	title="Machine Learning and Artificial Intelligence for Cell Biology Imaging",
	bookTitle="Trends in Biomathematics: Modeling Health Across Ecology, Social Interactions, and Cells: Selected Works from the BIOMAT Consortium Lectures, Kolympari, Greece, 2024",
	year="2025",
	publisher="Springer Nature Switzerland",
	address="Cham",
	pages="199--219",
	abstract="Live cell imaging is a key approach in cell biology to study dynamic cellular mechanisms and cell fate in physiological conditions and upon genetic or chemical alterations. Video recording allows the biologist to observe the dynamic behavior of cells and to quantify useful information, such as the number, size, and shape of cells and their dynamic changes across time. Consequently, time-lapse microscopy imaging is a relevant approach for biomedical applications.",
isbn="978-3-031-97461-8",
	doi="10.1007/978-3-031-97461-8_11",
	url="https://doi.org/10.1007/978-3-031-97461-8_11"
}

@InProceedings{Bernhardt2025ESG,
	author="Bernhardt, Brian Daniel and Marciano, Chiara and Gigli, Sara and Maddalena, Lucia and Guarracino, Mario Rosario",
	editor="D'Ambrosio, Antonio and de Rooij, Mark and De Roover, Kim and Iorio, Carmela and La Rocca, Michele",
	title="Understanding ESG Scores Through Network Analysis: A Study Using Graph Neural Networks",
	booktitle="Supervised and Unsupervised Statistical Data Analysis",
	year="2025",
	publisher="Springer Nature Switzerland",
	address="Cham",
	pages="37--47",
	abstract="Graph neural networks (GNNs) are powerful tools for analyzing graph-structured data and have numerous applications. In this study, GNNs are used to explore the connections between a set of Italian companies, focusing on how sharing decision-makers influence their Environmental, Social, and Governance (ESG) scores. The research compares various GNN models, including GraphSAGE, Graph Attention Networks, and Graph Neural Additive Networks, with traditional classification techniques such as kNN, Random Forest, Decision Trees, and Na{\"i}ve Bayes. The results demonstrate that GNNs provide a higher node classification accuracy and suggest that more central nodes tend to have higher ESG scores, indicating that companies with greater network connectivity may provide higher ESG performance. This suggests that sharing decision-makers could be a strategic tool to enhance efforts in sustainability and social responsibility.",
	isbn="978-3-032-03042-9"
}

@InProceedings{Marciano2025EGNAN,
	author="Marciano, Chiara and Bernhardt, Brian D.  and Maddalena, Lucia and Guarracino, Mario R.",
	editor="di Bella, Enrico and Gioia, Vincenzo and Lagazio, Corrado and Zaccarin, Susanna",
	title="EGNAN: An Enhanced Method for Interpretable Graph Neural Networks",
	booktitle="Statistics for Innovation III",
	year="2025",
	publisher="Springer Nature Switzerland",
	address="Cham",
	pages="457--462",
	abstract="Graph Neural Networks (GNNs) are increasingly used in various domains. However, their black-box nature limits interpretability. In this work, we propose EGNAN, a new interpretable method for GNNs. We provide a theoretical analysis of EGNAN, describing its key components. EGNAN is compared with the existing explainable Graph Neural Additive Network (GNAN), evaluating its performance and interpretability. Through experiments, we assess the models across different data distributions and graph characteristics, including variations in homophily and class imbalance. The results show that EGNAN is a promising classification method, combining high accuracy with reduced execution times compared to GNAN, while still ensuring interpretability. Additionally, graphical representations clearly depict the relationships between the target variable, features, and graph structure, enhancing the model's interpretability. This suggests that EGNAN could provide a valuable contribution to more interpretable and efficient graph-based learning models.",
	isbn="978-3-031-95995-0"
}

%%%%%%%%%%%%%%%%%%% 2024 %%%%%%%%%%%%%%%%%%% 
@article{Granata2024HELP,
    author = {Granata, Ilaria AND Maddalena, Lucia AND Manzo, Mario AND Guarracino, Mario Rosario AND Giordano, Maurizio},
    journal = {PLOS Computational Biology},
    publisher = {Public Library of Science},
    title = {HELP: A computational framework for labelling and predicting human common and context-specific essential genes},
    year = {2024},
    month = {09},
    volume = {20},
    doi = {10.1371/journal.pcbi.1012076},
    url = {https://doi.org/10.1371/journal.pcbi.1012076},
    pages = {1-23},
    abstract = {Machine learning-based approaches are particularly suitable for identifying essential genes as they allow the generation of predictive models trained on features from multi-source data. Gene essentiality is neither binary nor static but determined by the context. The databases for essential gene annotation do not permit the personalisation of the context, and their update can be slower than the publication of new experimental data. We propose HELP (Human Gene Essentiality Labelling & Prediction), a computational framework for labelling and predicting essential genes. Its double scope allows for identifying genes based on dependency or not on experimental data. The effectiveness of the labelling method was demonstrated by comparing it with other approaches in overlapping the reference sets of essential gene annotations, where HELP demonstrated the best compromise between false and true positive rates. The gene attributes, including multi-omics and network embedding features, lead to high-performance prediction of essential genes while confirming the existence of essentiality nuances.},
    number = {9},
}

@software{Giordano2024SW_HELP,
  author       = {Maurizio Giordano and Granata, Ilaria and Lucia, Maddalena},
  title        = {{giordamaug/HELP: Human Gene Essentiality Labelling \& Prediction}},
  month        = jul,
  year         = 2024,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.12697605},
  url          = {https://doi.org/10.5281/zenodo.12697605}
}


@article{Maddalena2024KneeBones3Dify,
  author = {Lucia Maddalena and Diego Romano and Francesco Gregoretti and Gianluca {De Lucia} and Laura Antonelli and Ernesto Soscia and Gabriele Pontillo and Carla Langella and Flavio Fazioli and Carla Giusti and Rosario Varriale},
  title = {KneeBones3Dify: Open-source software for segmentation and 3D reconstruction of knee bones from MRI data},
  keywords = {Knee bone segmentation, Python, GPU, Parallel computing, 3D bone models for printing},
  journal = {SoftwareX},
  volume = {27},
  pages = {101854},
  year = {2024},
  issn = {2352-7110},
  doi = {https://doi.org/10.1016/j.softx.2024.101854},
  url = {https://www.sciencedirect.com/science/article/pii/S2352711024002255},
abstract = {KneeBones3Dify is a Python software tool that supports detailed analysis of knee pathologies and preoperative planning for knee replacement surgery based on patient-specific 3D models. It produces printable 3D bones in a stereolithography file format by automatically segmenting the femur, patella, and tibia from high-resolution Magnetic Resonance (MR) images with nearly isotropic voxel dimensions. Our software avoids time-consuming and subjective manual segmentation by specialists, offering an accurate and efficient alternative employing GPU acceleration. We validated the results by computing objective metrics against the ground truth voxel-wise segmentation produced for a 3D MR image by specialists, who also confirmed the reconstruction accuracy qualitatively. KneeBones3Dify and annotated data are publicly available, enabling broader research and clinical practice use.}
}

@software{DeLucia2024SW_KneeBones3Dify,
  author       = {Gianluca De Lucia and Lucia Maddalena and Diego Romano and Francesco Gregoretti and Laura Antonelli},
  title        = {KneeBones3Dify 1.0},
  month        = jan,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {v1.0},
  doi          = {10.5281/zenodo.10522876},
  url          = {https://doi.org/10.5281/zenodo.10522876}
}

@dataset{Soscia2024Dataset_KneeBones3Dify,
  author       = {Soscia, Ernesto and Romano, Diego and Maddalena, Lucia and Gregoretti, Francesco and De Lucia, Gianluca and Antonelli, Laura},
  title        = {KneeBones3Dify-Annotated-Dataset v1.0.0},
  month        = jan,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {V1.0.0},
  doi          = {10.5281/zenodo.10534328},
  url          = {https://doi.org/10.5281/zenodo.10534328}
}

@book{Maddalena2024ReprintSI,
  editor    	= {Maddalena, Lucia and Antonelli, Laura},
  title     	= {Algorithms for Biomedical Image Analysis and Processing},
  publisher 	= {MDPI},
  year      	= {2024},
  note      	= {Reprint of the Special Issue Algorithms for Biomedical Image Analysis and Processing, published in Algorithms},
  isbn      	= {978-3-0365-9760-7},
  url       	= {https://doi.org/10.3390/books978-3-0365-9761-4},
  doi       	= {10.3390/books978-3-0365-9761-4},
}

@Article{Giordano2024EG,
  AUTHOR = {Giordano, Maurizio and Falbo, Emanuele and Maddalena, Lucia and Piccirillo, Marina and Granata, Ilaria},
  TITLE = {Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience},
  JOURNAL = {Biomolecules},
  VOLUME = {14},
  YEAR = {2024},
  NUMBER = {1},
  ARTICLE-NUMBER = {18},
  URL = {https://www.mdpi.com/2218-273X/14/1/18},
  ISSN = {2218-273X},
  DOI = {10.3390/biom14010018}
}

%%%%%%%%%%%%%%%%%%% 2023 %%%%%%%%%%%%%%%%%%% 
@Article{Antonelli2023Editorial,
  AUTHOR = {Antonelli, Laura and Maddalena, Lucia},
  TITLE = {Special Issue on "Algorithms for Biomedical Image Analysis and Processing"},
  JOURNAL = {Algorithms},
  VOLUME = {16},
  YEAR = {2023},
  NUMBER = {12},
  ARTICLE-NUMBER = {544},
  URL = {https://www.mdpi.com/1999-4893/16/12/544},
  ISSN = {1999-4893},
  DOI = {10.3390/a16120544}
}


@ARTICLE{Antonelli2023ALFI,
  author	={Antonelli, Laura and Polverino, Federica and Albu, Alexandra and Hada, Aroj and Asteriti, Italia A. and Degrassi, Francesca and Guarguaglini, Giulia and Maddalena, Lucia and Guarracino, Mario R.},
  journal	={Scientific Data}, 
  title		={{ALFI}: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells}, 
  year		={2023},
  volume	={10},
  number	={677},
  pages		={},
  doi		={10.1038/s41597-023-02540-1}}

@dataset{Antonelli2023Dataset_ALFI, 
  author	={Antonelli, Laura and Polverino, Federica and Albu, Alexandra and Hada, Aroj and Asteriti, Italia and Degrassi, Francesca and Guarguaglini, Giulia and Maddalena, Lucia and Guarracino, Mario}, 
  title		={ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells}, 
  url		={https://springernature.figshare.com/collections/ALFI_Cell_cycle_phenotype_annotations_of_label-free_time-lapse_imaging_data_from_cultured_human_cells/6436958/1}, 
  DOI		={10.6084/m9.figshare.c.6436958.v1}, 
  abstractNote	={ALFI (Annotations for Label-Free Images) is a dataset of images and annotations for label-free microscopy imaging. It consists of 29 time-lapse image sequences with various annotations (pixel-wise segmentation masks, object-wise bounding boxes, and tracking information), made publicly available to the scientific community through figshare.}, publisher={figshare}, 
  year		={2023}, 
  month		={Sep} 
} 


@incollection{GranataBIOMAT2023, 
  author 	= {I. Granata and M. Giordano and L. Maddalena and M. Manzo and M. R. Guarracino}, 
  title 	= {Network-Based Computational Modeling to Unravel Gene Essentiality}, 
  doi 		= {10.1007/978-3-031-33050-6_3}, 
  url 		= {https://doi.org/10.1007%2F978-3-031-33050-6_3}, 
  year 		= 2023, 
  publisher 	= {Springer Nature Switzerland}, 
  pages 	= {29--56}, 
  booktitle 	= {Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics}} 

@ARTICLE{SBVchallenge2023,
  author	={Khachatryan, Lusine and Xiang, Yang and Ivanov, Artem and Glaab, Enrico and Graham, Garrett and Granata, Ilaria and Giordano, Maurizio and Maddalena, Lucia and Piccirillo, Marina and Manipur, Ichcha and Baruzzo, Giacomo and Cappellato, Marco and Avot, Batiste and Stan, Adrian and Battey, James and Lo Sasso, Giuseppe and Boue, Stephanie and Ivanov, Nikolai V.  and Peitsch, Manuel C.  and Hoeng, Julia and Falquet, Laurent and Di Camillo, Barbara and Guarracino, Mario R.  and Ulyantsev, Vladimir and Sierro, Nicolas and Poussin, Carine}, 
  journal	={Scientific Reports}, 
  title		={Results and lessons learned from the sbv {IMPROVER} metagenomics diagnostics for inflammatory bowel disease challenge}, 
  year		={2023},
  volume	={13},
  number	={6303},
  pages		={},
  doi		={10.1038/s41598-023-33050-0}}

@Inbook{Manzo2023DataScience,
author		="Manzo, M and Giordano, M, and Maddalena, L, and Guarracino, MR, and Granata, I",
editor		="Dzemyda, G., Bernataviciene, J., Kacprzyk, J.",
title		="Novel Data Science Methodologies for Essential Genes Identification Based on Network Analysis",
bookTitle	="Data Science in Applications. Studies in Computational Intelligence",
year		="2023",
publisher	="Springer",
address		="Cham",
volume		="1084",
doi		="10.1007/978-3-031-24453-7\_7",
url		="https://link.springer.com/chapter/10.1007/978-3-031-24453-7\_7"}

@ARTICLE{Maddalena2023SNCS,
  author	={Maddalena, Lucia and Granata, Ilaria and Giordano, Maurizio and Manzo, Mario and Guarracino, Mario Rosario},
  journal	={SN Computer Science}, 
  title		={Integrating Different Data Modalities for the Classification of Alzheimer's Disease Stages}, 
  year		={2023},
  volume	={4},
  number	={249},
  pages		={},
  doi		={10.1007/s42979-023-01688-2}}

@ARTICLE{Manipur2023PPINs,
  author	={Manipur, Ichcha and Giordano, Maurizio and Piccirillo, Marina and Parashuraman, Seetharaman and Maddalena, Lucia},
  journal	={IEEE/ACM Transactions on Computational Biology and Bioinformatics}, 
  title		={Community Detection in Protein-Protein Interaction Networks and Applications}, 
  year		={2023},
  volume	={20},
  number	={1},
  pages		={217-237},
  doi		={10.1109/TCBB.2021.3138142}}

%%%%%%%%%%%%%%%%%%% 2022 %%%%%%%%%%%%%%%%%%% 
@ARTICLE{Giordano2022AMAI,
  author	={Giordano, Maurizio and Maddalena, Lucia and Manzo, Mario and Guarracino, Mario Rosario},
  journal	={Ann Math Artif Intell}, 
  title		={Adversarial attacks on graph-level embedding methods: a case study}, 
  year		={2022},
  volume	={},
  number	={},
  pages		={},
  doi		={10.1007/s10472-022-09811-4}}

@Inbook{Maddalena2022BIOMAT2021,
author		="Maddalena, L. and Giordano, M. and Manzo, M. and Guarracino, M. R.",
editor		="Mondaini, Rubem P.",
title		="Whole-Graph Embedding and Adversarial Attacks for Life Sciences",
bookTitle	="Trends in Biomathematics: Stability and Oscillations in Environmental, Social, and Biological Models: Selected Works from the BIOMAT Consortium Lectures, Rio de Janeiro, Brazil, 2021",
year		="2022",
publisher	="Springer International Publishing",
address		="Cham",
pages		="1--21",
abstract	="Networks provide a suitable model for many scientific and technological problems that require the representation of complex entities and their relations. Life sciences applications include systems biology, where molecular components are represented in integrated systems in which the interactions among them provide richer information than single components taken separately, or neuroimaging, where brain networks allow representing the connectivity between different brain locations. In the examples we focus on, a set of networks is available, with each network representing an entity (e.g., a molecule, a macro molecule, or a patient) and links expressing their relation in the chemical/biological domain.",
isbn		="978-3-031-12515-7",
doi		="10.1007/978-3-031-12515-7\_1",
url		="https://doi.org/10.1007/978-3-031-12515-7\_1"}

@ARTICLE{Granata2022TumorMet,
  author	={Granata, Ilaria and Manipur, Ichcha and Giordano, Maurizio and Maddalena, Lucia and Guarracino, Mario Rosario},
  journal	={Scientific Data}, 
  title		={TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models}, 
  year		={2022},
  volume	={9},
  number	={607},
  pages		={},
  doi		={10.1038/s41597-022-01702-x}}

@ARTICLE{Maddalena2022review,
AUTHOR 		= {Maddalena, Lucia and Antonelli, Laura and Albu, Alexandra and Hada, Aroj and Guarracino, Mario Rosario},
TITLE 		= {Artificial Intelligence for Cell Segmentation, Event Detection, and Tracking for Label-free Microscopy Imaging},
JOURNAL		= {Algorithms},
VOLUME 		= {15},
YEAR 		= {2022},
NUMBER 		= {9},
ARTICLE-NUMBER 	= {313},
URL 		= {https://www.mdpi.com/1999-4893/15/9/313},
ISSN 		= {1999-4893},
DOI 		= {10.3390/a15090313},
ABSTRACT 	= {Background: Time-lapse microscopy imaging is a key approach for an increasing number of biological and biomedical studies to observe the dynamic behavior of cells over time which helps quantify important data, such as the number of cells and their sizes, shapes, and dynamic interactions across time. Label-free imaging is an essential strategy for such studies as it ensures that native cell behavior remains uninfluenced by the recording process. Computer vision and machine/deep learning approaches have made significant progress in this area. Methods: In this review, we present an overview of methods, software, data, and evaluation metrics for the automatic analysis of label-free microscopy imaging. We aim to provide the interested reader with a unique source of information, with links for further detailed information. Results: We review the most recent methods for cell segmentation, event detection, and tracking. Moreover, we provide lists of publicly available software and datasets. Finally, we summarize the metrics most frequently adopted for evaluating the methods under exam. Conclusions: We provide hints on open challenges and future research directions.},}

@ARTICLE{Manipur2021Netpro2vec,
  author	= {Manipur, Ichcha and Manzo, Mario and Granata, Ilaria and Giordano, Maurizio and Maddalena, Lucia and Guarracino, Mario Rosario},
  journal	= {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, 
  title		= {Netpro2vec: a Graph Embedding Framework for Biomedical Applications}, 
  year		= {2022},
  volume	= {19},
  number	= {2},
  pages		= {729---740},
  doi		= {10.1109/TCBB.2021.3078089}
}

@Conference{Maddalena2022BIOIMAGING,
author          = "Maddalena, Lucia and Granata, Ilaria and Giordano, Maurizio and Manzo, Mario and Guarracino, Mario R. and {Alzheimer's Disease Neuroimaging Initiative (ADNI)}",
title           = "Classifying Alzheimer's Disease using MRIs and Transcriptomic Data",
booktitle       = {Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING,},
year            = {2022},
pages           = {70-79},
publisher       = {SciTePress},
organization    = {INSTICC},
doi             = {10.5220/0010902900003123},
isbn            = {978-989-758-552-4},
}

%%%%%%%%%%%%%%%%%%% 2021 %%%%%%%%%%%%%%%%%%% 
@InProceedings{Manzo2021LION15,
author 		= "Manzo, Mario and Giordano, Maurizio and Maddalena, Lucia and Guarracino, Mario R.",
editor 		= "Simos, Dimitris E.  and Pardalos, Panos M.  and Kotsireas, Ilias S.",
title  		= "Performance Evaluation of Adversarial Attacks on Whole-Graph Embedding Models",
booktitle	= "Learning and Intelligent Optimization",
year 		= "2021",
publisher 	= "Springer International Publishing",
address 	= "Cham",
series 		= "Lecture Notes in Computer Science",
volume 		= "12931",
pages 		= "219--236",
abstract 	= "Graph embedding techniques are becoming increasingly common in many fields ranging from scientific computing to biomedical applications and finance. These techniques aim to automatically learn low-dimensional representations for a variety of network analysis tasks. In literature, several methods (e.g., random walk-based, factorization-based, and neural network-based) show very promising results in terms of their usability and potential. Despite their spreading diffusion, little is known about their reliability and robustness, particularly when applied to the real world of data, where adversaries or malfunctioning/noisy data sources may supply deceptive data. The vulnerability emerges mainly by inserting limited perturbations in the input data when these lead to a dramatic deterioration in performance. In this work, we propose an analysis of different adversarial attacks in the context of whole-graph embedding. The attack strategies involve a limited number of nodes based on the role they play in the graph. The study aims to measure the robustness of different whole-graph embedding approaches to those types of attacks, when the network analysis task consists in the supervised classification of whole-graphs. Extensive experiments carried out on synthetic and real data provide empirical insights on the vulnerability of whole-graph embedding models to node-level attacks in supervised classification tasks.",
doi 		= "10.1007/978-3-030-92121-7\_19",
isbn 		= "978-3-030-92121-7"
}


@Inbook{Maddalena2021,
author		= "Maddalena, L.  and Manipur, I.  and Manzo, M.  and Guarracino, M. R.",
editor		= "Mondaini, Rubem P.",
title		= "On Whole-Graph Embedding Techniques",
bookTitle	= "Trends in Biomathematics: Chaos and Control in Epidemics, Ecosystems, and Cells: Selected Works from the 20th BIOMAT Consortium Lectures, Rio de Janeiro, Brazil, 2020",
year		= "2021",
publisher	= "Springer International Publishing",
address		= "Cham",
pages		= "115--131",
abstract	= "Networks provide suitable representative models in many applications, ranging from social to life sciences. Such representations are able to capture interactions and dependencies among variables or observations, thus providing simple and powerful modeling of phenomena. Whole-graph embedding involves the projection of graphs into a vector space, while retaining their structural properties. In recent years, several embedding techniques using graph kernels, matrix factorization, and deep learning architectures have been developed to learn low-dimensional graph representations. These embeddings can be used for feature extraction, graph clustering, or building classification models. In this chapter, we survey embedding techniques that jointly embed whole graphs for classification tasks. We compare them and evaluate their performance on undirected synthetic and real-world network datasets. The datasets and software adopted for our experiments are made publicly available for further comparisons.",
isbn		= "978-3-030-73241-7",
doi		= "10.1007/978-3-030-73241-7\_8",
url		= "https://doi.org/10.1007/978-3-030-73241-7\_8"
}

@InProceedings{MaddalenaBIOSTEC2021,
author		= "Maddalena, Lucia and Granata, Ilaria and Manipur, Ichcha and Manzo, Mario and Guarracino, Mario R.",
title		= "A Framework Based on Metabolic Networks and Biomedical Images Data to Discriminate Glioma Grades",
booktitle	= "Biomedical Engineering Systems and Technologies",
editor		= "Ye, Xuesong and Soares, Filipe and De Maria, Elisabetta and G{\'o}mez Vilda, Pedro and Cabitza, Federico and Fred, Ana and Gamboa, Hugo",
year		= "2021",
publisher	= "Springer International Publishing",
address		= "Cham",
pages		= "165--189",
abstract	= "Collecting and integrating information from different data sources is a successful approach to investigate complex biological phenomena and to address tasks such as disease subtyping, biomarker prediction, target, and mechanisms identification. Here, we describe an integrative framework, based on the combination of transcriptomics data, metabolic networks, and magnetic resonance images, to classify different grades of glioma, one of the most common types of primary brain tumors arising from glial cells. The framework is composed of three main blocks for feature sorting, choosing the best number of sorted features, and classification model building. We investigate different methods for each of the blocks, highlighting those that lead to the best results. Our approach demonstrates how the integration of molecular and imaging data achieves better classification performance than using the individual data-sets, also comparing results with state-of-the-art competitors. The proposed framework can be considered as a starting point for a clinically relevant grading system, and the related software made available lays the foundations for future comparisons.",
isbn		= "978-3-030-72379-8",
doi		= {10.1007/978-3-030-72379-8\_9}
}

%%%%%%%%%%%%%%%%%%% 2020 %%%%%%%%%%%%%%%%%%% 
@InProceedings{GranataMOTOR2020,
author		= "Granata, Ilaria and Guarracino, Mario Rosario and Maddalena, Lucia and Manipur, Ichcha",
title		= "Network Distances for Weighted Digraphs",
booktitle	= "Mathematical Optimization Theory and Operations Research",
editor		= "Kochetov, Yury and Bykadorov, Igor and Gruzdeva, Tatiana",
year		= "2020",
publisher	= "Springer International Publishing",
address		= "Cham",
series 		= "CCIS",
volume 		= "1275",
pages		= "389--408",
abstract	= "The interpretation of the biological mechanisms through the systems biology approach involves the representation of the molecular components in an integrated system, namely a network, where the interactions among them are much more informative than the single components. The definition of the dissimilarity between complex biological networks is fundamental to understand differences between conditions, states, and treatments. It is, therefore, challenging to identify the most suitable distance measures for this kind of analysis. In this work, we aim at testing several measures to define the distance among sample- and condition-specific metabolic networks. The networks are represented as directed, weighted graphs, due to the nature of the metabolic reactions. We used four different case studies and exploited Support Vector Machine classification to define the performance of each measure.",
doi 		= "10.1007/978-3-030-58657-7\_31",
isbn		= "978-3-030-58657-7"
}

@article{ManipurBMC2020,
author 		= "Ichcha Manipur and Ilaria Granata and Lucia Maddalena and Guarracino, Mario Rosario",
title 		= "Clustering analysis of tumor metabolic networks",
journal 	= "BMC Bioinformatics",
year 		= "2020",
issn 		= "1471-2105",
doi 		= "https://doi.org/10.1186/s12859-020-03564-9",
url 		= "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03564-9",
}

@article{MaddalenaPRL2020,
author 		= "Lucia Maddalena and Marco Gori and Sankar K. Pal",
title 		= "Pattern recognition and beyond: Alfredo Petrosino's scientific results",
journal 	= "Pattern Recognition Letters",
volume 		= "138",
pages 		= "659--669",
year 		= "2020",
issn 		= "0167-8655",
doi 		= "https://doi.org/10.1016/j.patrec.2020.07.032",
url 		= "http://www.sciencedirect.com/science/article/pii/S0167865520302865",
keywords 	= "Computer Vision, Deep Learning, Fuzzy Logic, High-Performance Computing, Machine Learning, Neuro-Fuzzy, Neural Network, Soft and Granular Computing",
abstract 	= "We summarize the main scientific contributions of our friend and colleague Alfredo Petrosino, full professor in computer science at the University of Naples Parthenope, Italy. They mainly cover topics in high-performance computing, neural network models, soft and granular computing, computer vision, and machine learning. We also highlight how most of his research activity lays the foundation for biometry and its applications."
}

@Inbook{Granata2020,
author		= "Granata, I. and Guarracino, M. R. and Maddalena, L. and Manipur, I. and Pardalos, P. M.",
editor		= "Mondaini, Rubem P.",
title		= "On Network Similarities and Their Applications",
bookTitle	= "Trends in Biomathematics: Modeling Cells, Flows, Epidemics, and the Environment: Selected Works from the BIOMAT Consortium Lectures, Szeged, Hungary, 2019",
year		= "2020",
publisher	= "Springer International Publishing",
address		= "Cham",
pages		= "23--41",
abstract	= "Network representations of biological systems have provided a more in-depth understanding of cellular and molecular interaction mechanisms. The comparison of complex biological networks is an ongoing area of research. It is necessary to identify similarity measures which are particularly suited for biological network analysis. We focus our survey on network similarity measures in the context of directed, weighted, and structurally similar biological networks. We summarize the underlying mechanisms and features exploited by such measures, which help to bring out the differences or similarities between networks. We also provide a brief overview of studies which have been performed using these measures on different biological network data. Finally, we show performance results obtained by applying the measures on tumor metabolic networks.",
isbn		= "978-3-030-46306-9",
doi		= "10.1007/978-3-030-46306-9\_3",
url		= "https://doi.org/10.1007/978-3-030-46306-9\_3"
}

@conference{MaddalenaBIOIMAGING2020,
author		= {Lucia Maddalena. and Ilaria Granata. and Ichcha Manipur. and Mario Manzo. and Guarracino, M.},
title		= {Glioma Grade Classification via Omics Imaging},
booktitle	= {Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,},
year		= {2020},
pages		= {82-92},
publisher	= {SciTePress},
organization	= {INSTICC},
doi		= {10.5220/0009167700820092},
isbn		= {978-989-758-398-8},
url       	= {https://www.scitepress.org/Link.aspx?doi=10.5220/0009167700820092},
}

@Article{GranataAMAI2019,
author		= "Granata, Ilaria and Guarracino, Mario R. and Kalyagin, Valery A. and Maddalena, Lucia and Manipur, Ichcha and Pardalos, Panos M.",
title		= "Model simplification for supervised classification of metabolic networks",
journal		= "Annals of Mathematics and Artificial Intelligence",
year		= "2020",
volume		= "88",
pages		= "91-104",
issn		= "1573-7470",
doi		= "10.1007/s10472-019-09640-y",
url 		= "https://doi.org/10.1007/s10472-019-09640-y",
}

%%%%%%%%%%%%%%%%%%% 2019 %%%%%%%%%%%%%%%%%%% 

@article{AntonelliBSPC2019,
author 		= "Laura Antonelli and Mario Rosario Guarracino and Lucia Maddalena and Mara Sangiovanni",
title 		= "Integrating imaging and omics data: A review",
journal 	= "Biomedical Signal Processing and Control",
volume 		= "52",
pages 		= "264--280",
year 		= "2019",
issn 		= "1746-8094",
doi 		= "https://doi.org/10.1016/j.bspc.2019.04.032",
url 		= "http://www.sciencedirect.com/science/article/pii/S1746809419301326",
keywords 	= "Omics imaging, Imaging genomics, Radiogenomics, Biomedical imaging, Omics data",
}

@article{MaddalenaMTA2019,
  author	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {Self-Organizing Background Subtraction Using Color and Depth Data},
  journal   	= {Multimedia Tools and Applications},
  volume    	= {78},
  number    	= {9},
  pages     	= {11927--11948},
  year      	= {2019},
  url       	= {http://link.springer.com/article/10.1007/s11042-018-6741-7},
  doi       	= {10.1007/s11042-018-6741-7},
}

@article{GuarracinoJBHI2017,
  author    	= {Mario Rosario Guarracino and  Lucia Maddalena},
  title     	= {{SDI+}: a Novel Algorithm for Segmenting Dermoscopic Images},
  journal   	= {IEEE Journal of Biomedical and Health Informatics},
  volume    	= {23},
  number    	= {22},
  pages    	= {481-488},
  year      	= {2019},
  url       	= {https://ieeexplore.ieee.org/document/8301391/},
}


%%%%%%%%%%%%%%%%%%% 2018 %%%%%%%%%%%%%%%%%%% 

@article{MaddalenaJImaging2018,
  author	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {Background Subtraction for Moving Object Detection in {RGBD} Data: {A} Survey},
  journal   	= {J. Imaging},
  volume    	= {4},
  number    	= {5},
  pages     	= {71},
  year      	= {2018},
  url       	= {https://doi.org/10.3390/jimaging4050071},
  doi       	= {10.3390/jimaging4050071},
}


%%%%%%%%%%%%%%%%%%% 2017 %%%%%%%%%%%%%%%%%%% 

@article{PetrosinoPRLeditorial2017,
  author    	= {Alfredo Petrosino and  Lucia Maddalena and  Thierry Bouwmans},
  title     	= {Editorial-Scene background modeling and initialization},
  journal   	= {Pattern Recognition Letters},
  volume   	= {96},
  pages     	= {1--2},
  year      	= {2017},
  url       	= {https://doi.org/10.1016/j.patrec.2017.05.032},
  doi       	= {10.1016/j.patrec.2017.05.032},
}

@article{BouwmansPRL2017,
  author    	= {Thierry Bouwmans and Lucia Maddalena and Alfredo Petrosino},
  title     	= {Scene background initialization: {A} taxonomy},
  journal   	= {Pattern Recognition Letters},
  volume   	= {96},
  pages     	= {3--11},
  year      	= {2017},
  url       	= {https://doi.org/10.1016/j.patrec.2016.12.024},
  doi       	= {10.1016/j.patrec.2016.12.024},
}

@article{JodoinTIP2017,
  author    	= {Pierre{-}Marc Jodoin and Lucia Maddalena and Alfredo Petrosino and Yi Wang},
  title     	= {Extensive Benchmark and Survey of Modeling Methods for Scene Background  Initialization},
  journal   	= {{IEEE} Trans. Image Processing},
  volume    	= {26},
  number    	= {11},
  pages     	= {5244--5256},
  year      	= {2017},
  url       	= {https://doi.org/10.1109/TIP.2017.2728181},
  doi       	= {10.1109/TIP.2017.2728181},
}

@inproceedings{CamplaniICIAP2017,
  author    	= {Massimo Camplani and Lucia Maddalena and  Gabriel Moy{\`{a}} Alcover and  Alfredo Petrosino and  Luis Salgado},
  title     	= {A Benchmarking Framework for Background Subtraction in {RGBD} Videos},
  booktitle 	= {New Trends in Image Analysis and Processing - {ICIAP} International Workshops: WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS,  and MADiMa 2017, Catania, Italy, September 11-15, 2017, Revised Selected  Papers},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {10590},
  publisher 	= {Springer},
  pages     	= {219--229},
  year     	= {2017},
  url       	= {https://doi.org/10.1007/978-3-319-70742-6\_21},
  doi       	= {10.1007/978-3-319-70742-6\_21},
}

@inproceedings{MaddalenaICIAP2017,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {Exploiting Color and Depth for Background Subtraction},
  booktitle 	= {New Trends in Image Analysis and Processing - {ICIAP} International Workshops: WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS,  and MADiMa 2017, Catania, Italy, September 11-15, 2017, Revised Selected  Papers},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {10590},
  publisher 	= {Springer},
  pages     	= {254--265},
  year      	= {2017},	
  url       	= {https://doi.org/10.1007/978-3-319-70742-6\_24},
  doi       	= {10.1007/978-3-319-70742-6\_24},
}

@article{GuarracinoArxiv2017,
  author    	= {Mario Rosario Guarracino and  Lucia Maddalena},
  title     	= {Segmenting Dermoscopic Images},
  journal   	= {CoRR},
  volume    	= {abs/1703.03186},
  year      	= {2017},
  url       	= {http://arxiv.org/abs/1703.03186},
  archivePrefix = {arXiv},
  eprint    	= {1703.03186},
}


%%%%%%%%%%%%%%%%%%% 2016 %%%%%%%%%%%%%%%%%%% 

@inproceedings{MaddalenaICPR2016,
  author    	= {Lucia Maddalena and  Alfredo Petrosino},
  title     	= {Extracting a background image by a multi-modal scene background model},
  booktitle 	= {23rd International Conference on Pattern Recognition, {ICPR} 2016, Canc{\'{u}}n, Mexico, December 4-8, 2016},
  publisher 	= {{IEEE}},
  isbn      	= {978-1-5090-4847-2},
  pages     	= {143--148},
  year      	= {2016},
  url       	= {https://doi.org/10.1109/ICPR.2016.7899623},
  doi       	= {10.1109/ICPR.2016.7899623},
}


%%%%%%%%%%%%%%%%%%% 2015 %%%%%%%%%%%%%%%%%%% 

@inproceedings{MaddalenaICIAP2015,
  author    	= {Lucia Maddalena and  Alfredo Petrosino},
  title     	= {Towards Benchmarking Scene Background Initialization},
  booktitle 	= {New Trends in Image Analysis and Processing - {ICIAP} 2015 Workshops: BioFor, CTMR, RHEUMA, ISCA,  MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings},
  editor    	= {Vittorio Murino and Enrico Puppo and Diego Sona and  Marco Cristani and  Carlo Sansone},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {9281},
  publisher 	= {Springer},
  pages     	= {469--476},
  year      	= {2015},
  url       	= {https://doi.org/10.1007/978-3-319-23222-5\_57},
  doi       	= {10.1007/978-3-319-23222-5\_57},
}

@inproceedings{Casalino2015,
  author	= {Casalino, Laura and D'Ambra, Pasqua and Guarracino, Mario R. and Irpino, Antonio and Maddalena, Lucia and Maiorano, Francesco and Minchiotti, Gabriella and Jorge Patriarca, Eduardo},
  editor	= {Zazzu, Valeria and Ferraro, Maria Brigida and Guarracino, Mario R.},
  title		= {Image Analysis and Classification for High-Throughput Screening of Embryonic Stem Cells},
  bookTitle	= {Mathematical Models in Biology: Bringing Mathematics to Life},
  year		= {2015},
  publisher	= {Springer International Publishing},
  pages		= {17--31},
}

@inproceedings{DiNardoWIRN2015,
  author    	= {Di Nardo, E. and Maddalena, Lucia and  Petrosino, Alfredo},
  title     	= {Video-Based Access Control by Automatic License Plate Recognition},
  booktitle 	= {Advances in Neural Networks: Computational and Theoretical Issues},
  editor    	= {Bassis, Simone and Esposito, Anna and Morabito, Francesco Carlo},
  series    	= {Smart Innovation, Systems and Technologies},
  volume    	= {37},
  publisher 	= {Springer International Publishing},
  pages     	= {103--117},
  year      	= {2015},
  url       	= {https://doi.org/10.1007/978-3-319-18164-6\_11},
  doi       	= {10.1007/978-3-319-18164-6\_11},
}


%%%%%%%%%%%%%%%%%%% 2014 %%%%%%%%%%%%%%%%%%% 

@article{MaddalenaCVIU2014,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {The {3dSOBS+} algorithm for moving object detection},
  journal   	= {Computer Vision and Image Understanding},
  volume    	= {122},
  pages     	= {65--73},
  year      	= {2014},
  url       	= {https://doi.org/10.1016/j.cviu.2013.11.006},
  doi       	= {10.1016/j.cviu.2013.11.006},
}

@article{MaddalenaPRL2014,
  author    	= {Lucia Maddalena and Alfredo Petrosino and  Francesco Russo},
  title     	= {People counting by learning their appearance in a multi-view camera  environment},
  journal   	= {Pattern Recognition Letters},
  volume    	= {36},
  pages     	= {125--134},
  year      	= {2014},
  url       	= {https://doi.org/10.1016/j.patrec.2013.10.006},
  doi       	= {10.1016/j.patrec.2013.10.006},
}

@article{FeroneSMC2014,
  author    	= {Alessio Ferone and Lucia Maddalena},
  title     	= {Neural Background Subtraction for Pan-Tilt-Zoom Cameras},
  journal   	= {{IEEE} Trans. Systems, Man, and Cybernetics: Systems},
  volume    	= {44},
  number    	= {5},
  pages     	= {571--579},
  year      	= {2014},
  url       	= {https://doi.org/10.1109/TSMC.2013.2280121},
  doi       	= {10.1109/TSMC.2013.2280121},
}

@incollection{MaddalenaChapter2014,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title 	= {Background Model Initialization for Static Cameras},
  booktitle 	= {Background Modeling and Foreground Detection for Video Surveillance},
  publisher 	= {Chapman \& Hall/CRC},
  editor 	= {Thierry Bouwmans and Fatih Porikli and Benjamin Hoferlin and Antoine Vacavant},
  pages 	= {3-1--3-16},
  year 		= {2014},
  url       	= {https://doi.org/10.1201/b17223-5},
  doi       	= {10.1201/b17223-5},
}


%%%%%%%%%%%%%%%%%%% 2013 %%%%%%%%%%%%%%%%%%% 

@article{MaddalenaTNN2013,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {Stopped Object Detection by Learning Foreground Model in Videos},
  journal   	= {{IEEE} Trans. Neural Netw. Learning Syst.},
  volume    	= {24},
  number    	= {5},
  pages     	= {723--735},
  year      	= {2013},
  url       	= {https://doi.org/10.1109/TNNLS.2013.2242092},
  doi       	= {10.1109/TNNLS.2013.2242092},
}

@proceedings{PetrosinoICIAP2013,
  editor    	= {Alfredo Petrosino and  Lucia Maddalena and Pietro Pala},
  title     	= {New Trends in Image Analysis and Processing - {ICIAP} 2013 International Workshops, Naples, Italy, September 9-13, 2013.  Proceedings},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {8158},
  publisher 	= {Springer},
  year      	= {2013},
  url       	= {https://doi.org/10.1007/978-3-642-41190-8},
  doi       	= {10.1007/978-3-642-41190-8},
  isbn      	= {978-3-642-41189-2},
}


%%%%%%%%%%%%%%%%%%% 2012 %%%%%%%%%%%%%%%%%%% 

@inproceedings{MaddalenaCVPR2012,
  author    	= {Lucia Maddalena and  Alfredo Petrosino},
  title     	= {The {SOBS} algorithm: What are the limits?},
  booktitle 	= {2012 {IEEE} Computer Society Conference on Computer Vision and Pattern  Recognition Workshops, Providence, RI, USA, June 16-21, 2012},
  publisher 	= {{IEEE} Computer Society},
  isbn      	= {978-1-4673-1611-8},
  pages     	= {21--26},
  year      	= {2012},
  url       	= {https://doi.org/10.1109/CVPRW.2012.6238922},
  doi       	= {10.1109/CVPRW.2012.6238922},
}

@inproceedings{FeroneWIRN2012,
  author    	= {Alessio Ferone and Lucia Maddalena and Alfredo Petrosino},
  title     	= {Neural Moving Object Detection by Pan-Tilt-Zoom Cameras},
  booktitle 	= {Neural Nets and Surroundings - 22nd Italian Workshop on Neural Nets,  {WIRN} 2012, Vietri sul Mare, Salerno, Italy, May 17-19, 2012},
  editor    	= {Bruno Apolloni and Simone Bassis and Anna Esposito and Francesco Carlo Morabito},
  series    	= {Smart Innovation, Systems and Technologies},
  volume    	= {19},
  publisher 	= {Springer},
  isbn      	= {978-3-642-35466-3},
  pages     	= {129--138},
  year      	= {2012},
  url       	= {https://doi.org/10.1007/978-3-642-35467-0\_14},
  doi       	= {10.1007/978-3-642-35467-0\_14},
}

@book{PalSCVS2012,
  editor    	= {Pal, Sankar Kumar and Petrosino, Alfredo and Lucia Maddalena},
  title     	= {Handbook on Soft Computing for Video Surveillance},
  publisher 	= {Chapman and Hall/CRC},
  year      	= {2012},
  isbn      	= {978-1-4398-5684-0},
  url       	= {https://doi.org/10.1201/b11631},
  doi       	= {10.1201/b11631},
}

@incollection{MaddalenaSCVS2012,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title 	= {Neural Networks in Video Surveillance: A Perspective View},
  booktitle 	= {Handbook on Soft Computing for Video Surveillance},
  publisher 	= {Chapman \& Hall/CRC},
  editor    	= {Pal, Sankar Kumar and Petrosino, Alfredo and Lucia Maddalena},
  isbn      	= {978-1-4398-5684-0},
  pages 	= {59--78},
  year 		= {2012},
}


%%%%%%%%%%%%%%%%%%% 2011 %%%%%%%%%%%%%%%%%%% 

@article{CozzaSiM2011,
  author 	= {Cozza, Valentina and Guarracino, Mario Rosario and Maddalena, Lucia and Baroni, Adone},
  title 		= {Dynamic clustering detection through multi-valued descriptors of dermoscopic images},
  journal 	= {Statistics in Medicine},
  volume 	= {30},
  number 	= {20},
  publisher 	= {John Wiley & Sons, Ltd},
  issn 		= {1097-0258},
  pages 	= {2536--2550},
  year 		= {2011},
  url 		= {http://dx.doi.org/10.1002/sim.4285},
  doi 		= {10.1002/sim.4285},
}


%%%%%%%%%%%%%%%%%%% 2010 %%%%%%%%%%%%%%%%%%% 

@article{MaddalenaNCA2010,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection},
  journal   	= {Neural Computing and Applications},
  volume    	= {19},
  number    	= {2},
  pages     	= {179--186},
  year      	= {2010},
  url       	= {https://doi.org/10.1007/s00521-009-0285-8},
  doi       	= {10.1007/s00521-009-0285-8},
}

@inproceedings{GemignaniUCHPC2010,
  author    	= {Giorgio Gemignani and Lucia Maddalena and  Alfredo Petrosino},
  title     	= {Real-Time Stopped Object Detection by Neural Dual Background Modeling},
  booktitle 	= {Euro-Par 2010 Parallel Processing Workshops - HeteroPar, HPCC, HiBB, CoreGrid, UCHPC, HPCF, PROPER, CCPI, VHPC, Ischia, Italy, August 31-September 3, 2010, Revised Selected Papers},
  editor    	= {Mario R. Guarracino and Fr{\'{e}}d{\'{e}}ric Vivien and Jesper Larsson Tr{\"{a}}ff and Mario Cannataro and Marco Danelutto and Anders Hast and Francesca Perla and Andreas Kn{\"{u}}pfer and  Beniamino Di Martino and  Michael Alexander},  
  series    	= {Lecture Notes in Computer Science},
  volume    	= {6586},
  publisher 	= {Springer},
  isbn      	= {978-3-642-21877-4},
  pages     	= {357--364},
  year      	= {2010},
  url       	= {https://doi.org/10.1007/978-3-642-21878-1\_44},
  doi       	= {10.1007/978-3-642-21878-1\_44},
}


%%%%%%%%%%%%%%%%%%% 2009 %%%%%%%%%%%%%%%%%%% 

@article{MaddalenaPR2009,
  author    	= {Lucia Maddalena and Alfredo Petrosino and Giuliano Laccetti},
  title     	= {A fusion-based approach to digital movie restoration},
  journal   	= {Pattern Recognition},
  volume    	= {42},
  number    	= {7},
  pages     	= {1485--1495},
  year      	= {2009},
  url       	= {https://doi.org/10.1016/j.patcog.2008.10.026},
  doi       	= {10.1016/j.patcog.2008.10.026},
}

@inproceedings{MaddalenaACIVS2009,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {Self Organizing and Fuzzy Modelling for Parked Vehicles Detection},
  booktitle 	= {Advanced Concepts for Intelligent Vision Systems, 11th International Conference, {ACIVS} 2009, Bordeaux, France, September 28 - October 2, 2009. Proceedings},
  editor    	= {Jacques Blanc{-}Talon and Wilfried Philips and Dan C. Popescu and Paul Scheunders},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {5807},
  publisher 	= {Springer},
  isbn      	= {978-3-642-04696-4},
  pages     	= {422--433},
  year      	= {2009},
  url       	= {https://doi.org/10.1007/978-3-642-04697-1\_39},
  doi       	= {10.1007/978-3-642-04697-1\_39},
}

@inproceedings{MaddalenaICIAP2009,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {{3D} Neural Model-Based Stopped Object Detection},
  booktitle 	= {Image Analysis and Processing - {ICIAP} 2009, 15th International Conference Vietri sul Mare, Italy, September 8-11, 2009, Proceedings},
  editor    	= {Pasquale Foggia and Carlo Sansone and  Mario Vento},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {5716},
  publisher 	= {Springer},
  isbn      	= {978-3-642-04145-7},
  pages     	= {585--593},
  year      	= {2009},
  url       	= {https://doi.org/10.1007/978-3-642-04146-4\_63},
  doi       	= {10.1007/978-3-642-04146-4\_63},
}

@inproceedings{MaddalenaWILF2009,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {Multivalued Background/Foreground Separation for Moving Object Detection},
  booktitle 	= {Fuzzy Logic and Applications, 8th International Workshop, {WILF} 2009, Palermo, Italy, June 9-12, 2009, Proceedings},
  editor    	= {Vito Di Ges{\`{u}} and Sankar K. Pal and Alfredo Petrosino},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {5571},
  publisher 	= {Springer},
  isbn     	= {978-3-642-02281-4},
    pages     	= {263--270},
  year      	= {2009},
  url       	= {https://doi.org/10.1007/978-3-642-02282-1\_33},
  doi       	= {10.1007/978-3-642-02282-1\_33},
}

@inproceedings{MaddalenaWIRN2009,
  author    	= {Lucia Maddalena and  Alfredo Petrosino},
  title     	= {A {3D} Neural Model for Video Analysis},
  booktitle 	= {Neural Nets {WIRN09} - Proceedings of the 19th Italian Workshop on  Neural Nets, Vietri sul Mare, Salerno, Italy, May 28-30 2009},
  editor    	= {Bruno Apolloni and Simone Bassis and Francesco Carlo Morabito},
  series    	= {Frontiers in Artificial Intelligence and Applications},
  volume    	= {204},
  publisher 	= {{IOS} Press},
  isbn      	= {978-1-60750-072-8},
  pages     	= {101--109},
  year      	= {2009},
  url       	= {https://doi.org/10.3233/978-1-60750-072-8-101},
  doi       	= {10.3233/978-1-60750-072-8-101},
}


%%%%%%%%%%%%%%%%%%% 2008 %%%%%%%%%%%%%%%%%%% 

@article{MaddalenaIJPRAI2008,
  author    	= {Lucia Maddalena and  Alfredo Petrosino and Alessio Ferone},
  title     	= {Object Motion Detection and Tracking by an Artificial Intelligence Approach},
  journal   	= {{International Journal of Pattern Recognition and Artificial Intelligence}},
  volume    	= {22},
  number    	= {5},
  pages     	= {915--928},
  year      	= {2008},
  url       	= {https://doi.org/10.1142/S0218001408006612},
  doi       	= {10.1142/S0218001408006612},
}

@article{MaddalenaIVC2008,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {Restoration of blue scratches in digital image sequences},
  journal   	= {Image and Vision Computing},
  publisher   	= {Elsevier},
  volume    	= {26},
  number    	= {10},
  pages     	= {1314--1326},
  year      	= {2008},
  url       	= {https://doi.org/10.1016/j.imavis.2006.04.013},
  doi       	= {10.1016/j.imavis.2006.04.013},
}

@article{MaddalenaTIP2008,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications},
  journal   	= {{IEEE} Trans. Image Processing},
  volume    	= {17},
  number    	= {7},
  pages     	= {1168--1177},
  year      	= {2008},
  url       	= {https://doi.org/10.1109/TIP.2008.924285},
  doi       	= {10.1109/TIP.2008.924285},
}

@inproceedings{MaddalenaICANN2008,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {A Self-organizing Neural System for Background and Foreground Modeling},
  booktitle 	= {Artificial Neural Networks - {ICANN} 2008 , 18th International Conference,  Prague, Czech Republic, September 3-6, 2008, Proceedings, Part {I}},
  editor    	= {Vera Kurkov{\'{a}} and  Roman Neruda and Jan Koutn{\'{\i}}k},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {5163},
  publisher 	= {Springer},
  isbn      	= {978-3-540-87535-2},
  pages     	= {652--661},
  year      	= {2008},
  url       	= {https://doi.org/10.1007/978-3-540-87536-9\_67},
  doi       	= {10.1007/978-3-540-87536-9\_67},
}

@inproceedings{MaddalenaKES2008,
  author    	= {Lucia Maddalena and  Alfredo Petrosino},
  title     	= {Neural Model-Based Segmentation of Image Motion},
  booktitle 	= {Knowledge-Based Intelligent Information and Engineering Systems, 12th International Conference, {KES} 2008, Zagreb, Croatia, September 3-5, 2008, Proceedings, Part {I}},
  editor    	= {Ignac Lovrek and Robert J. Howlett and Lakhmi C. Jain},
  series    	= {Lecture Notes in Computer Science},	
  volume    	= {5177},
  publisher 	= {Springer},
  isbn      	= {978-3-540-85562-0},
  pages     	= {57--64},
  year      	= {2008},
  url       	= {https://doi.org/10.1007/978-3-540-85563-7\_13},
  doi       	= {10.1007/978-3-540-85563-7\_13},
}


%%%%%%%%%%%%%%%%%%% 2008 %%%%%%%%%%%%%%%%%%% 

@inproceedings{MaddalenaBVAI2007,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {A Self-organizing Approach to Detection of Moving Patterns for Real-Time Applications},
  booktitle 	= {Advances in Brain, Vision, and Artificial Intelligence, Second International Symposium, {BVAI} 2007, Naples, Italy, October 10-12, 2007, Proceedings},
  editor    	= {Francesco Mele and Giuliana Ramella and Silvia Santillo and Francesco Ventriglia},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {4729},
  publisher 	= {Springer},
  isbn      	= {978-3-540-75554-8},
  pages     	= {181--190},
  year      	= {2007},
  url       	= {https://doi.org/10.1007/978-3-540-75555-5\_18},
  doi       	= {10.1007/978-3-540-75555-5\_18},
}

@inproceedings{MaddalenaICIAP2007,
  author    	= {Lucia Maddalena and Alfredo Petrosino},
  title     	= {Moving Object Detection for Real-Time Applications},
  booktitle 	= {14th International Conference on Image Analysis and Processing {(ICIAP} 2007), 10-14 September 2007, Modena, Italy},
  editor    	= {Rita Cucchiara},
  publisher 	= {{IEEE} Computer Society},
  isbn      	= {0-7695-2877-5},
  pages     	= {542--547},
  year      	= {2007},
  url       	= {https://doi.org/10.1109/ICIAP.2007.4362834},
  doi       	= {10.1109/ICIAP.2007.4362834},
}

@inproceedings{DiDonnaWILF2007,
  author    	= {Fabio Di Donna and Lucia Maddalena and Alfredo Petrosino},
  title     	= {About the Embedding of Color Uncertainty in {CBIR} Systems},
  booktitle 	= {Applications of Fuzzy Sets Theory, 7th International Workshop on Fuzzy Logic and Applications, {WILF} 2007, Camogli, Italy, July 7-10, 2007, Proceedings},
  editor    	= {Francesco Masulli and Sushmita Mitra and Gabriella Pasi},  
  series    	= {Lecture Notes in Computer Science},
  volume    	= {4578},
  publisher 	= {Springer},
  isbn      	= {978-3-540-73399-7},
  pages     	= {394--403},
  year      	= {2007},
  url       	= {https://doi.org/10.1007/978-3-540-73400-0\_50},
  doi       	= {10.1007/978-3-540-73400-0\_50},
}


%%%%%%%%%%%%%%%%%%% 2005 %%%%%%%%%%%%%%%%%%% 

@inproceedings{LaccettiCAMP2005,
  author    	= {Giuliano Laccetti and  Lucia Maddalena and  Alfredo Petrosino},
  title     	= {{P-LSR:} {A} Parallel Algorithm for Line Scratch Restoration},
  booktitle 	= {Seventh International Workshop on Computer Architectures for Machine  Perception {(CAMP} 2005), 4-6 July 2005, Palermo, Italy},
  publisher 	= {{IEEE} Computer Society},
  isbn      	= {0-7695-2255-6},
  pages     	= {225--230},
  year      	= {2005},
  url       	= {https://doi.org/10.1109/CAMP.2005.37},
  doi       	= {10.1109/CAMP.2005.37},
}

@inproceedings{LaccettiICIAP2005,
  author    	= {Giuliano Laccetti and  Lucia Maddalena and  Alfredo Petrosino},
  title     	= {Removing Line Scratches in Digital Image Sequences by Fusion Techniques},
  booktitle 	= {Image Analysis and Processing - {ICIAP} 2005, 13th International Conference, Cagliari, Italy, September 6-8, 2005, Proceedings},
  editor    	= {Fabio Roli and  Sergio Vitulano},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {3617},
  publisher 	= {Springer},
  isbn      	= {3-540-28869-4},
  pages     	= {695--702},
  year      	= {2005},
  url       	= {https://doi.org/10.1007/11553595\_85},
  doi       	= {10.1007/11553595\_85},
}


%%%%%%%%%%%%%%%%%%% 2004 %%%%%%%%%%%%%%%%%%% 

@inproceedings{LaccettiICCSA2004,
  author    	= {Giuliano Laccetti and  Lucia Maddalena and  Alfredo Petrosino},
  title     	= {Parallel/Distributed Film Line Scratch Restoration by Fusion Techniques},
  booktitle 	= {Computational Science and Its Applications - {ICCSA} 2004, International Conference, Assisi, Italy, May 14-17, 2004, Proceedings, Part {II}},
  editor    	= {Antonio Lagan{\`{a}} and Marina L. Gavrilova and Vipin Kumar and Youngsong Mun and Chih Jeng Kenneth Tan and Osvaldo Gervasi},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {3044},
  publisher 	= {Springer},
  isbn      	= {3-540-22056-9},
  pages     	= {525--535},
  year      	= {2004},
  url       	= {https://doi.org/10.1007/978-3-540-24709-8\_56},
  doi       	= {10.1007/978-3-540-24709-8\_56},
}


%%%%%%%%%%%%%%%%%%% 2001 %%%%%%%%%%%%%%%%%%% 

@inproceedings{MaddalenaICIAP2001,
  author    	= {Lucia Maddalena},
  title     	= {Efficient Methods for Scratch Removal in Image Sequences},
  booktitle 	= {11th International Conference on Image Analysis and Processing {(ICIAP} 2001), 26-28 September 2001, Palermo, Italy},
  publisher 	= {{IEEE} Computer Society},
  isbn      	= {0-7695-1183-X},
  pages     	= {547--552},
  year      	= {2001},
  url       	= {https://doi.org/10.1109/ICIAP.2001.957067},
  doi       	= {10.1109/ICIAP.2001.957067},
}


%%%%%%%%%%%%%%%%%%% 1998 %%%%%%%%%%%%%%%%%%% 

@incollection{diSerafino1998,
  author    	= {Daniela di Serafino and Lucia Maddalena and Paul Messina and Almerico Murli},
  title 	= {Some Perspectives on High-Performance Mathematical Software},
  booktitle 	= {High Performance Algorithms and Software in Nonlinear Optimization},
  publisher 	= {Kluwer Academic Publishers},
  editor    	= {de Leone, R. and Murli, A. and Pardalos, P.M. and Toraldo, G.},
  isbn      	= {0-7923-5483-4},
  pages 	= {1--23},
  year 		= {1998},
  url 		= {http://www.springer.com/mathematics/book/978-0-7923-5483-3},
}


%%%%%%%%%%%%%%%%%%% 1997 %%%%%%%%%%%%%%%%%%% 

@article{DApuzzoNPSC1997,
  author    	= {Marco D'Apuzzo and  Lucia Maddalena},
  title     	= {A parallel algorithm for parametric cubic {B}-spline curves interpolation},
  journal   	= {Neural Parallel {\&} Scientific Comp.},
  publisher	= {Dynamic Publishers Inc, USA},
  issn      	= {1061-5396},
  volume    	= {5},
  number    	= {1-2},
  pages     	= {201--220},
  year      	= {1997},
  url       	= {http://dl.acm.org/citation.cfm?id=251685},
}

@incollection{DApuzzoCAGD1997,
  author    	= {Marco D'Apuzzo and  Lucia Maddalena},
  title 	= {Parallelization Strategies for the {B}-Spline Curve Interpolation Problem},
  booktitle 	= {Curves and Surfaces with Applications in CAGD},
  publisher 	= {Vanderbilt University Press},
  editor    	= {Le M\'ehaut\'e, A. and Rabut, C. and Schumaker, L.L.},
  isbn      	= {0-8265-1293-3},
  pages 	= {1--8},
  year 		= {1997},
  url 		= {https://catalyst.library.jhu.edu/catalog/bib\_1971158},
}

@inproceedings{DiSerafinoEuropar1997,
  author    	= {Daniela di Serafino and  Lucia Maddalena and  Almerico Murli},
  title     	= {{PINEAPL:} {A} {E}uropean Project to Develop a Parallel Numerical Library for Industrial Applications},
  booktitle 	= {Euro-Par '97 Parallel Processing, Third International Euro-Par Conference,  Passau, Germany, August 26-29, 1997, Proceedings},
  editor    	= {Christian Lengauer and Martin Griebl and Sergei Gorlatch},
  series    	= {Lecture Notes in Computer Science},
  volume    	= {1300},
  publisher 	= {Springer},
  isbn      	= {3-540-63440-1},
  pages     	= {1333--1339},
  year      	= {1997},
  url       	= {https://doi.org/10.1007/BFb0002891},
  doi       	= {10.1007/BFb0002891},
}