Journal Papers (selected)
Amato, D., Calderaro, S., Lo Bosco, G., Rizzo, R., Vella, F. (2024). Explainable Histopathology Image Classification with Self-organizing Maps: A Granular Computing Perspective. Cognitive Computation [10.1007/s12559-024-10312-1]
Amato, D., Lo Bosco, G., Giancarlo, R. (2023). Neural networks as building blocks for the design of efficient learned indexes. NEURAL COMPUTING & APPLICATIONS [10.1007/s00521-023-08841-1].
Amato, D., Lo Bosco, G., Giancarlo, R. (2023). Standard versus uniform binary search and their variants in learned static indexing: The case of the searching on sorted data benchmarking software platform. SOFTWARE-PRACTICE & EXPERIENCE, 53(2), 318-346 [10.1002/spe.3150].
Giacalone, G., Barra, M., Bonanno, A., Basilone, G., Fontana, I., Calabrò, M., Genovese, S., Ferreri, R., Buscaino, G., Mazzola, S., Noormets, R.,Nuth, C., Lo Bosco, G., Rizzo, R.,Aronica, S. (2022). A pattern recognition approach to identify biological clusters acquired by acoustic multi-beam in Kongsfjorden. ENVIRONMENTAL MODELLING & SOFTWARE, 152, 1-10 [10.1016/j.envsoft.2022.105401].
Fontana, I., Barra, M., Bonanno, A., Giacalone, G., Rizzo, R., Mangoni, O., Genovese, S., Basilone, G., Ferreri, R., Mazzola, S., Lo Bosco, G., Aronica, S. (2022). Automatic classification of acoustically detected krill aggregations: a case study from Southern Ocean. ENVIRONMENTAL MODELLING & SOFTWARE, , 151, art. no. 105357 [10.1016/j.envsoft.2022.105357].
Lo Bosco, G., Pilato, G., Schicchi, D. (2021). DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages. ARRAY, 12, 1-10 [10.1016/j.array.2021.100097].
Alfano, M., Lenzitti, B., Lo Bosco, G., Muriana, C., Piazza, T., & Vizzini, G. (2020). Design, Development and Validation of a System for Automatic Help to Medical Text Understanding. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 138, 1-8 [10.1016/j.ijmedinf.2020.104109].
Amato, D., Lo Bosco, G., & Riccardo, R. (2020). CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification. BMC BIOINFORMATICS, 21(8), 326 [10.1186/s12859-020-03627-x].
Aronica, S., Fontana, I., Giacalone, G., Lo Bosco, G., Rizzo, R., Mazzola, S., et al. (2019). Identifying small pelagic Mediterranean fish schools from acoustic and environmental data using optimized artificial neural networks. ECOLOGICAL INFORMATICS, 50, 149-161.
Chen, H., Albergante, L., Hsu, J.Y., Lareau, C.A., Lo Bosco, G., Guan, J., et al. (2019). Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. NATURE COMMUNICATIONS, 10(1), 1-14 [10.1038/s41467-019-09670-4].
Di Gangi, M., Lo Bosco, G., & Pilato, G. (2019). Effectiveness of data-driven induction of semantic spaces and traditional classifiers for sarcasm detection. NATURAL LANGUAGE ENGINEERING, 25(2), 257-285 [10.1017/S1351324919000019].
Di Gangi, M., Lo Bosco, G., & Rizzo, R. (2018). Deep learning architectures for prediction of nucleosome positioning from sequences data. BMC BIOINFORMATICS, 19(14), 127-135 [10.1186/s12859-018-2386-9].
Fiannaca, A., La Paglia, L., La Rosa, M., Lo Bosco, G., Renda, G., Rizzo, R., et al. (2018). Deep learning models for bacteria taxonomic classification of metagenomic data. BMC BIOINFORMATICS, 19, 61-76 [10.1186/s12859-018-2182-6].
Giancarlo, R., Lo Bosco, G., & Utro, F. (2015). Bayesian versus data driven model selection for microarray data. NATURAL COMPUTING, 14(3), 393-402 [10.1007/s11047-014-9446-5].
Cipolla, M., Lo Bosco, G., Millonzi, F., & Valenti, C. (2014). An Island Strategy for Memetic Discrete Tomography Reconstruction. INFORMATION SCIENCES, 257, 357-368 [10.1016/j.ins.2013.05.019].
Pinello, L., Lo Bosco, G., & Yuan Guo, C. (2014). Applications of alignment-free methods in epigenomics. BRIEFINGS IN BIOINFORMATICS, 15(3), 419-430 [10.1093/bib/bbt078].
Giancarlo, R., Lo Bosco, G., Pinello, L., & Utro, F. (2013). A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis. BMC BIOINFORMATICS, 14(SUPPL.1), 1-14 [10.1186/1471-2105-14-S1-S6].
Pinello, L., Lo Bosco, G., Hanlon, B., & Yuan, G. (2011). A motif-independent metric for DNA sequence specificity. BMC BIOINFORMATICS, 12 [10.1186/1471-2105-12-408].
Sala, A., Toto, M., Pinello, L., Gabriele, A., DI BENEDETTO, V., Ingrassia, A., et al. (2011). Genome-wide characterization of chromatin binding and nucleosome spacing activity of the nucleosome remodelling ATPase ISWI. EMBO JOURNAL, 30, 1766-1777 [10.1038/emboj.2011.98].
Di Gesù, V., Lo Bosco, G., Millonzi, F., & Valenti, C. (2010). A memetic approach to discrete tomography from noisy projections. PATTERN RECOGNITION, 43(9), 3073-3082 [doi:10.1016/j.patcog.2010.04.001].
Di Gesù, V., Lo Bosco, G., Pinello, L., Yuan, G.C., & Corona, D. (2009). A Multi-Layer Method to Study Genome-Scale Positions of Nucleosomes. GENOMICS, 93(2), 140-145.
Lo Bosco, G. (2007). An integrated fuzzy cells-classifier. IMAGE AND VISION COMPUTING, 25, 214-219 [10.1016/j.imavis.2006.01.031].
Di Gesù, V., Giancarlo, R., Lo Bosco, G., Raimondi, A., & Scaturro, D. (2005). GenClust: A Genetic Algorithm for Clustering Gene Expression Data. BMC BIOINFORMATICS, 6 (289), 1-21 [10.1186/1471-2105-6-289].
Di Gesù, V., & Lo Bosco, G. (2005). A Genetic Integrated Fuzzy Classifier. PATTERN RECOGNITION LETTERS, 26(4), 411-420 [10.1016/j.patrec.2004.08.004].
Book Chapters
Giancarlo, R., Lo Bosco, G., Pinello, L., & Utro, F. (2011). The Three Steps of Clustering in the Post-Genomic Era: A Synopsis. In R. Rizzo, & P. Lisboa (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics, 7th International Meeting, CIBB 2010, Palermo, Italy, September 2010 Revised Selected Papers (pp. 13-30). Heidelberg : Springer Verlag [10.1007/978-3-642-21946-7_2].
Amato, D., Di Gangi, M.A., Fiannaca, A., La Paglia, L., La Rosa, M., Lo Bosco, G., et al. (2021). Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Issues. In M. Elloumi (eds), Deep Learning for Biomedical Data Analysis (pp. 27-59) [10.1007/978-3-030-71676-9_2].
Guest Edited Special Issue and Conference Proceedings
Zavidovique, B., Lo Bosco, G. (2011), “SCIENCE: IMAGe IN AcTION”, Proceedings of the 7th International Workshop on Data Analysis in Astronomy “Livio Scars and Vito Di Gesù”, Erice (Italy), World Scientific Publishing, Singapore, [13-978-981-4383-28-8].
Di Gesù, V., Lo Bosco, G., Maccarone, M.C. (2007), “Modelling and Simulation in Science”, Proceedings of the 6th International Workshop on Data Analysis in Astronomy <<Livio Scarsi>>, Erice (Italy), World Scientific Publishing, Singapore, [13-978-981-277-944-1].
Urso A., Fiannaca A., La Rosa M., La Paglia L., Lo Bosco G., & Rizzo R. (2020). BITS2019: The sixteenth annual meeting of the Italian Society of Bioinformatics. BMC BIOINFORMATICS, 21(8), 1-10 [10.1186/s12859-020-03708-x].
Fulantelli G., Burgos D., Casalino G., Cimitile M., Lo Bosco G., Taibi D., (2023) Higher Education Learning Methodologies and Technologies Online, Proceedings of the 4th International Conference, HELMeTO 2022, Palermo (Italy), Springer Nature [978-3-031-29800-4].
Conference papers
Amato, D., Calderaro, S., Lo Bosco, G., Rizzo, R., Vella, F. (2024) Bacteria Taxonomic Classification using Graph Neural Networks. EAIS 2024, Madrid, Spain, 2024 (pp. 1-6) [10.1109/EAIS58494.2024.10569104].
Calderaro, S., Lo Bosco, G., Rizzo, R., Vella, F. (2023) Visualization and Analysis of Transformer Attention. CEUR Workshop Proceedings, 3563, (pp. 42-49).
Calderaro, S., Lo Bosco, G., Vella, F., Rizzo, R. (2023). Breast Cancer Histologic Grade Identification by Graph Neural Network Embeddings. In I. Rojas, O. Valenzuela, F. Rojas Ruiz, L.J. Herrera, F. Ortuño (a cura di), Bioinformatics and Biomedical Engineering, 10th International Work-Conference, IWBBIO 2023, Meloneras, Gran Canaria, Spain, July 12–14, 2023, Proceedings, Part II (pp. 283-296) [10.1007/978-3-031-34960-7_20].
Calderaro, S., Lo Bosco, G., Rizzo, R., Vella, F. (2022). Deep Metric Learning for Transparent Classification of Covid-19 X-Ray Images. In The16th International Conference on Signal-Image Technology & Internet-Based Systems SITIS 2022 (pp. 300-307). IEEE [10.1109/SITIS57111.2022.00052].
Calderaro, S., Lo Bosco, G., Rizzo, R., Vella, F. (2022). Deep Metric Learning for Histopathological Image Classification. In 2022 IEEE Eighth International Conference on Multimedia Big Data (BigMM) BigMM 2022 (pp. 57-64) [10.1109/BigMM55396.2022.00016].
Barbera, R., Condorelli, F., Di Gregorio, G., Di Piazza, G., Farella, M., Lo Bosco, G., et al. (2022). A Pipeline for the Implementation of Immersive Experience in Cultural Heritage Sites in Sicily. In R. Furferi, L. Governi, Y. Volpe, K. Seymour, A. Pelagotti, F. Gherardini (a cura di), The Future of Heritage Science and Technologies ICT and Digital Heritage (pp. 178-191) [10.1007/978-3-031-20302-2_14].
Farella, M., Chiazzese, G., Lo Bosco, G. (2022). Question Answering with BERT: designing a 3D virtual avatar for Cultural Heritage exploration. In IEEE Melecon 2022 proceedings (pp. 770-774) [10.1109/MELECON53508.2022.9843028].
Barbera, R., Condorelli, F., Di Gregorio, G., Di Piazza, G., Farella, M., Lo Bosco, G., et al. (2022). A Case Study for the Design and Implementation of Immersive Experiences in Support of Sicilian Cultural Heritage. In P.L. Mazzeo, E. Frontoni, S. Sclaroff, C. Distante (a cura di), Image Analysis and Processing, ICIAP International Workshops, Lecce, Italy, May 23–27, 2022 Revised Selected Papers, Part I (pp. 174-185) [10.1007/978-3-031-13321-3_16].
Amato, D., Lo Bosco, G., Giancarlo, R. (2022). Learned Sorted Table Search and Static Indexes in Small Model Space. In S. Bandini, F. Gasparini, V. Mascardi, M. Palmonari, G. Vizzari (a cura di), AIxIA 2021 – Advances in Artificial Intelligence, 20th International Conference of the Italian Association for Artificial Intelligence, Virtual Event, December 1–3, 2021, Revised Selected Papers (pp. 462-477). Springer [10.1007/978-3-031-08421-8_32].
Amato, D., Lo Bosco, G., & Giancarlo, R. (2022). On the Suitability of Neural Networks as Building Blocks for the Design of Efficient Learned Indexes. In L. Iliadis, C. Jayne, A. Tefas, & E. Pimenidis (eds), Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600 (pp. 115-127). Springer, Cham [10.1007/978-3-031-08223-8_10].
D'Alessandro, A., Di Benedetto, A., Lo Bosco, G., & Figlioli, A. (2022). An Active Learning Approach for Classifying Explosion Quakes. In 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2022, 25-26 May 2022, Larnaca, Cyprus (pp. 1-6) [10.1109/EAIS51927.2022.9787519].
Calderaro, S., Lo Bosco, G., Rizzo, R., Vella, F. (2021) . Fuzzy Clustering of Histopathological Images Using Deep Learning Embeddings, CEUR Workshop Proceedings, 3074.
Fontana I., Giacalone G., Rizzo R., Barra M., Mangoni O., Bonanno A., et al. (2021). Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 65-74). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-68780-9_7].
Giacalone G., Lo Bosco G., Barra M., Bonanno A., Buscaino G., Noormets R., et al. (2021). Pattern Classification from Multi-beam Acoustic Data Acquired in Kongsfjorden. In A. Del Bimbo, R. Cucchiara, S. Sclaroff, H. FarinellaTao Mei Bertini, J. Escalante, & R. Vezzani (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 55-64). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-68780-9_6].
Cuzzocrea A., Lo Bosco G., Maiorana M., Pilato G., & Schicchi D. (2021). Towards a deep-learning-based methodology for supporting satire detection. In Proceedings - DMSVIVA 2021: 27th International DMS Conference on Visualization and Visual Languages (pp. 92-96). Knowledge Systems Institute Graduate School, KSI Research Inc. [10.18293/DMSVIVA2021-016].
Casalino, G., Cuzzocrea, A., Lo Bosco, G., Maiorana, M., Pilato, G., & Schicchi, D. (2021). A Novel Approach for Supporting Italian Satire Detection Through Deep Learning. Flexible Query Answering Systems, 14th International Conference, FQAS 2021, Bratislava, Slovakia, September 19–24, 2021, Proceedings (pp. 170-181) [10.1007/978-3-030-86967-0_13].
Megna A.L., Schicchi D., Lo Bosco G., & Pilato G. (2021). A Controllable Text Simplification System for the Italian Language. In Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021 (pp. 191-194). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSC50631.2021.00040].
Fiannaca, A., La Paglia, L., La Rosa, M., Lo Bosco, G., Rizzo, R., & Urso, A. (2020). Identification of Key miRNAs in Regulation of PPI Networks. In M. Raposo, P. Ribeiro, S. Sério, A. Staiano, & A. Ciaramella (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018 (pp. 107-117) [10.1007/978-3-030-34585-3_10].
Amato, D., Di Gangi, M.A., Lo Bosco, G., & Rizzo, R. (2020). Recurrent Deep Neural Networks for Nucleosome Classification. In M. Raposo, P. Ribeiro, S. Sério, A. Staiano, & A. Ciaramella (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics, 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018 (pp. 118-127) [10.1007/978-3-030-34585-3_11].
Schicchi, D., Pilato, G., & Lo Bosco, G. (2020). Attention-based Model for Evaluating the Complexity of Sentences in English Language. In 20TH IEEE MEDITERRANEAN ELETROTECHNICAL CONFERENCE Melecon 2020 (pp. 221-225) [10.1109/MELECON48756.2020.9140531].
Schicchi D., Pilato G., & Lo Bosco G. (2020). Deep neural attention-based model for the evaluation of italian sentences complexity. In Proceedings - 14th IEEE International Conference on Semantic Computing, ICSC 2020 (pp. 253-256). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSC.2020.00053].
Alcamo T., Cuzzocrea A., Lo Bosco G., Pilato G., & Schicchi D. (2020). Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora. In The 22nd International Conference on Information Integration and Web-based Applications & Services (pp. 91-96). Association for Computing Machinery [10.1145/3428757.3429144].
D'Alessandro A., Scudero S., Vitale G., Di Benedetto A., & Lo Bosco G. (2020). Optimization of Low-Cost Monitoring Systems for On-Site Earthquake Early-Warning of Critical Infrastructures. In O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blečić, D. Taniar, et al. (eds), Computational Science and Its Applications – ICCSA 2020 (pp. 963-975). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-58802-1_69].
MacMahon, S.T., Alfano, M., Lenzitti, B., Lo Bosco, G., McCaffery, F., Taibi, D., et al. (2019). Improving Communication in Risk Management of Health Information Technology Systems by means of Medical Text Simplification. In Proceedings - International Symposium on Computers and Communications (pp. 1135-1140). The Institute of Electrical and Electronics Engineers, IEEE [10.1109/ISCC47284.2019.8969670].
Schicchi, D., Lo Bosco, G., & Pilato, G. (2019). Machine Learning Models for Measuring Syntax Complexity of English Text. In A.V. Samsonovich (eds), Biologically Inspired Cognitive Architectures 2019, Proceedings of the Tenth Annual Meeting of the BICA Society (pp. 449-454) [10.1007/978-3-030-25719-4_59].
Argo, A., Arrigo, M., Bucchieri, F., Cappello, F., Di Paola, F., Farella, M., et al. (2019). Augmented Reality Gamification for Human Anatomy. In H. Sobke, M. Gentile, & M. Allegra (eds), Games and Learning Alliance : 7th International Conference, GALA 2018 (pp. 409-413). Cham : Springer [10.1007/978-3-030-11548-7_38].
Cuzzocrea, A., Lo Bosco, G., Pilato, G., & Schicchi, D. (2019). Multi-class Text Complexity Evaluation via Deep Neural Networks. In H. Yin, D. Camacho, P. Tino, A.J. Tallón-Ballesteros, R. Menezes, & R. Allmendinger (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2019, 20th International Conference Manchester, UK, November 14–16, 2019 Proceedings, Part II (pp. 313-322) [10.1007/978-3-030-33617-2_32].
Lo Bosco, G., Pilato, G., & Schicchi, D. (2019). A Recurrent Deep Neural Network Model to measure Sentence Complexity for the Italian Language. In A. Chella, I. Infantino, & A. Lieto (eds), AIC 2018, Artificial Intelligence and Cognition 2018 - Proceedings of the 6th International Workshop on Artificial Intelligence and Cognition (pp. 90-97).
Alfano, M., Lenzitti, B., Lo Bosco, G., & Taibi, D. (2018). Development and Practical Use of a Medical Vocabulary-Thesaurus-Dictionary for Patient Empowerment. In B. Rachev, & A. Smrikarov (eds), Proceedings of the 19th International Conference on Computer Systems and Technologies (pp. 88-93). New York : Association for Computing Machinery [10.1145/3274005.3274017].
Arrigo, M., Cappello, F., Di Paola, F., Farella, M., Lo Bosco, G., Saguto, D., et al. (2018). HEART MOBILE LEARNING. In L. Gómez Chova, A. López Martínez, & I. Candel Torres (eds), EDULEARN18 : conference proceedings (pp. 10899-10905). Valencia : IATED Academy.
Lo Bosco, G., Pilato, G., & Schicchi, D. (2018). A sentence based system for measuring syntax complexity using a recurrent deep neural network. In CEUR Workshop Proceedings (pp. 95-101). CEUR.
Lo Bosco, G., Rizzo, R., Fiannaca, A., La Rosa, M., & Urso, A. (2018). Variable Ranking Feature Selection for the Identification of Nucleosome Related Sequences. In A. Benczúr, B. Thalheim, T. Horváth, S. Chiusano, T. Cerquitelli, C. Sidló, et al. (eds), New Trends in Databases and Information Systems (pp. 314-324). Springer Verlag [10.1007/978-3-030-00063-9_30].
Di Gangi, M., Gaglio, S., La Bua, C., Lo Bosco, G., & Rizzo, R. (2017). A Deep Learning Network for Exploiting Positional Information in Nucleosome Related Sequences. In I. Rojas, & F. Ortuño (eds), Bioinformatics and Biomedical Engineering, 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part II (pp. 524-533) [10.1007/978-3-319-56154-7_47].
Chiavetta, F., Lo Bosco, G., & Pilato, G. (2017). A Layered Architecture for Sentiment Classification of Products Reviews in Italian Language. In V. Monfort, K.H. Krempels, T.A. Majchrzak, & P. Traverso (eds), Web Information Systems and Technologies, 12th International Conference, WEBIST 2016, Rome, Italy, April 23–25, 2016, Revised Selected Papers (pp. 120-141). Springer Verlag [10.1007/978-3-319-66468-2_7].
Lo Bosco, G., Rizzo, R., Fiannaca, A., La Rosa, M., & Urso, A. (2017). A Deep Learning Model for Epigenomic Studies. In K. Yetongnon, A. Dipanda, R. Chbeir, G. De Pietro, & L. Gallo (eds), Signal-Image Technologies and Internet-Based System, International IEEE Conference on (pp. 688-692). Institute of Electrical and Electronics Engineers Inc. [10.1109/SITIS.2016.115].
Chiavetta, F., Lo Bosco, G., & Pilato, G. (2016). A Lexicon-based Approach for Sentiment Classification of Amazon Books Reviews in Italian Language. In Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST 2016) (pp. 159-170). Scitepress [10.5220/0005915301590170].
Lo Bosco, G. (2016). Alignment Free Dissimilarities for Nucleosome Classification. In C. Angelini, P.M. Rancoita, & S. Rovetta (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics (pp. 114-128). Springer [10.1007/978-3-319-44332-4_9].
Lo Bosco, G., & Di Gangi, M. (2016). Deep Learning Architectures for DNA Sequence Classification. In A. Petrosino, V. Loia, & W. Pedrycz (eds), Fuzzy Logic and Soft Computing Applications, 11th International Workshop, WILF 2016, Naples, Italy, December 19–21, 2016, Revised Selected Papers (pp. 162-171). Springer [10.1007/978-3-319-52962-2 14].
Alfano, M., Lenzitti, B., Lo Bosco, G., & Taibi, D. (2016). A Framework for Opening Data and Creating Advanced Services in the Health and Social Fields. In CompSysTech '16 Proceedings of the 17th International Conference on Computer Systems and Technologies 2016 (pp. 57-64) [10.1145/2983468.2983473].
Alfano, M., Lenzitti, B., & Lo Bosco, G. (2015). U-MedSearch: A Meta Search Engine of Medical Content for Different Users and Learning Needs. In Proceedings of International Conference on e-Learning'15.
Lo Bosco, G., & Pinello, L. (2015). A New Feature Selection Methodology for K-mers Representation of DNA Sequences. In C. DI Serio (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics (pp. 99-108). Springer Verlag [10.1007/978-3-319-24462-4_9].
Lo Bosco, G., & La Neve, D. (2015). Alignment free Dissimilarities for sequence classification. In C. Angelini, E. Bongcam-Rudloff, A. Decarli, P. Rancoita, & S. Rovetta (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2015 (pp. 1-5). Department of Informatics, University of Salerno and Istituto per le Applicazioni del Calcolo “Mauro Picone” CNR.
Alfano, M., Lenzitti, B., Lo Bosco, G., & Perticone, V. (2015). An Automatic System for Helping Health Consumers to Understand Medical Texts. In A. Fred, H. Gamboa, C. Verdier, M. Bienkiewicz, & D. Elias (eds), Healthinf 2015, 8th international conference on health informatics, proceedings (pp. 622-627). Science and Technology Publications [10.5220/0005283606220627].
Alfano, M., Lenzitti, B., & Lo Bosco, G. (2014). A web search methodology for health consumers. In CompSysTech '14: Proceedings of the 15th International Conference on Computer Systems and Technologies (pp. 150-157). Association for Computing Machinery [10.1145/2659532.2659600].
Alfano, M., Lenzitti, B., Lo Bosco, G., & Perticone, V. (2014). Facilitating text understanding for e-learning users. In Proceedings of the International Conference on E-Learning e-Learning' 14 (pp. 83-88).
Lo Bosco, G., & Pinello, L. (2014). A new feature selection strategy for K-mers sequence representation. In C. Di Serio, P. Liò, S. Richardson, & R. Tagliaferri (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2014 (pp. 1-6).
Benvegna, F., Lo Bosco, G., & Tegolo, D. (2013). Dissimilarity Measures for the Identification of Earthquake Focal Mechanisms. In A. Petrosino (eds), Image Analysis and Processing – ICIAP 2013 (pp. 500-509). Berlin : Springer [10.1007/978-3-642-41184-7_51].
Benvegna, F., D'Alessandro, A., Lo Bosco, G., Luzio, D., Pinello, L., & Tegolo, D. (2011). A New Dissimilarity Measure for Clustering Seismic Signals. In G. Maino, & G.L. Foresti (eds), Image Analysis and Processing – ICIAP 2011 (pp. 434-443). Heidelberg : Springer Verlag [10.1007/978-3-642-24088-1_45].
Cipolla, M., Lo Bosco, G., Millonzi, F., & Valenti, C. (2011). A Memetic Island Model for Discrete Tomography Reconstruction. In A.M. Fanelli, W. Pedrycz, & A. Petrosino (eds), Fuzzy logic and Applications, 9th International Workshop, WILF 2011 Trani, Italy, August 2011. Proceedings (pp. 261-268). Heidelberg : Springer Verlag [10.1007/978-3-642-23713-3_33].
Giancarlo, R., Lo Bosco, G., Pinello, L., & Utro, F. (2011). The Three Steps of Clustering in the Post-Genomic Era: A Synopsis. In R. Rizzo, & P. Lisboa (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics, 7th International Meeting, CIBB 2010, Palermo, Italy, September 2010 Revised Selected Papers (pp. 13-30). Heidelberg : Springer Verlag [10.1007/978-3-642-21946-7_2].
Giancarlo, R., Lo Bosco, G., & Pinello, L. (2010). Distance Functions, Clustering Algorithms and Microarray Data Analysis. In C. Blum, & R. Battiti (eds), Learning and Intelligent Optimization 4th International Conference, LION 4, Venice, Italy, January 18-22, 2010. Selected Papers (pp. 125-138). Heidelberg : Springer [10.1007/978-3-642-13800-3_10].
Di Gesù, V., Lo Bosco, G., & Pinello, L. (2009). Interval Length Analysis in Multi Layer Model. In F. Masulli, R. Tagliaferri, & G.M. Verkhivker (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics, 5th International Meeting, CIBB 2008 Vietri sul Mare, Italy, October 3-4, 2008 Revised Selected Papers (pp. 114-122) [10.1007/978-3-642-02504-4_10].
Lo Bosco, G., & Pinello, L. (2009). A Fuzzy One Class Classifier for Multi Layer Model. In V. Di Gesu, S.K. Pal, & A. Petrosino (eds), Fuzzy Logic and Applications, 8th International Workshop, WILF 2009, Palermo, Italy, June 9-12, 2009 Proceedings (pp. 124-131) [10.1007/978-3-642-02282-1_16]."
Di Gesù, V., Lo Bosco, G., Millonzi, F., & Valenti, C. (2008). A Memetic Algorithm for Binary Image Reconstruction. In V.E. Brimkov, R.P. Barneva, & H.A. Hauptman (eds), Combinatorial Image Analysis,12th International Workshop, IWCIA 2008, Buffalo, NY, USA, April 7-9, 2008. Proceedings (pp. 384-395) [10.1007/978-3-540-78275-9_34].
Di Gesù, V., Lo Bosco, G., Millonzi, F., & Valenti, C. (2008). Discrete Tomography Reconstruction Through a New Memetic Algorithm. In M. Giacobini (eds), Applications of Evolutionary Computing (pp. 347-352). Springer-Verlag [10.1007/978-3-540-78761-7_36].
Di Gesù, V., Friedman, J., & Lo Bosco, G. (2008). Intruder Pattern Identification. In 2008 19th International Conference on Pattern Recognition (pp. 1-4). IEEE Computer Society [10.1109/ICPR.2008.4761050].
Di Gesù, V., Lo Bosco, G., & Pinello, L. (2008). A one class classifier for Signal identification: a biological case study. In I. Lovrek, R.J. Howlett, & L. Jain (eds), Knowledge-Based Intelligent Information and Engineering Systems, 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, Proceedings, Part III (pp. 747-754) [10.1007/978-3-540-85567-5-93].
Corona, D., Di Gesù, V., Lo Bosco, G., Pinello, L., Collesano, M., & Yuan, G. (2008). A Multi-layer model to study Genome-Scale Positions of Nucleosomes. In V. Di Gesù, G. Lo Bosco, & M. Maccarone (eds), Modelling and Simulation in Science (pp. 169-177). Singapore : World Scientific.
Corona, D., Di Gesù, V., Lo Bosco, G., Pinello, L., & Yuan, G. (2007). A new Multi-Layers Method to Analyze Gene Expression. In H.R.J. Apolloni B. (eds), Lecture Notes in Computer Science, 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007 (pp. 862-869). Berlin / Heidelberg : Springer [10.1007/978-3-540-74829-8].
Corona, D., Di Gesù, V., Lo Bosco, G., Pinello, L., & Yuan, G. (2007). Studying Nucleosomes Positioning by a Multi-Layer Model. In Grid Open Days at the University of Palermo (pp.81-86). Catania : Consorzio COMETA.
Di Gesù, V., & Lo Bosco, G. (2007). Combining one class fuzzy KNN’s. In F. Masulli, S. Mitra, & G. Pasi (eds), Applications of Fuzzy Sets Theory (pp. 152-160) [10.1007/978-3-540-73400-0_19].
Lo Bosco, G. (2006). An Integrated fuzzy Cells-classifier. In V. Di Gesú, F. Masulli, & A. Petrosino (eds), Fuzzy Logic and Applications, 5th International Workshop, WILF 2003, Naples, Italy, October 9-11, 2003, Revised Selected Papers (pp. 263-270). Springer Verlag.
Di Gesù, V., Lenzitti, B., Lo Bosco, G., & Tegolo, D. (2006). Comparison Of Different Cooperation Strategies in the Prey-Predator Problem. In CAMPS 2006 - International Workshop on Computer Architecture for Machine Perception and Sensing, Conference Proceedings (pp. 108-112). The Institute of Electrical and Electronics Engineers, IEEE [10.1109/CAMP.2007.4350364].
Lo Bosco, G. (2005). PGAC: A Parallel Genetic Algorithm for Data Clustering. In V. Di Gesù, & D. Tegolo (eds), Seventh International Workshop on Computer Architecture for Machine Perception (CAMP'05) (pp. 283-287). LOS ALAMITOS : IEEE Computer Society [10.1109/CAMP.2005.41].
Di Gesù, V., & Lo Bosco, G. (2005). Image Segmentation based on Genetic Algorithms Combination. In F. Roli, & S. Vitulano (eds), Image Analysis and Processing – ICIAP 2005, 13th International Conference, Cagliari, Italy, September 6-8, 2005. Proceedings (pp. 352-359). Springer [10.1007/11553595_43].
Tripodo, C., Paulli, M., Sirotti, M., Lo Bosco, G., Federico, M., & Franco, V. (2005). Nationwide interobserver variation in the diagnosis of follicular lymphoma: a report from the pathologists of GISL (Gruppo Italiano Studio Linfomi). In 9th International Conference on malignant lymphoma (pp.58-58).
Casanova, A., Di Gesù, V., Lo Bosco, G., & Vitulano, S. (2005). Entropy measures in Image Classification. In VITULANO S. (a cura di), Human and Machine Perception: Communication, Interaction, and Integration (pp. 89-103).
Di Gesù, V., & Lo Bosco, G. (2003). Experiments on a Prey predator system. In A. Bonarini, F. Masulli, & G. Pasi (eds), Advances in Soft Computing – Soft Computing Applications (pp. 71-79). Berlin Heidelberg : Springer-Verlag.
Di Gesù, V., & Lo Bosco, G. (2003). Integrated fuzzy classification. In FIFTH INTERNATIONAL CONFERENCE ON ADVANCES IN PATTERN RECOGNITION ICAPR-2003 (pp.448-452). New Delhi : Allied Publishers.
Di Gesù, V., Lo Bosco, G., & Zavidovique, B. (2003). Classification based on Iterative Object Symmetry Transform. In Proceedings - 12th International Conference on Image Analysis and Processing, ICIAP 2003 (pp. 44-49). IEEE computer society press [10.1109/ICIAP.2003.1234023].
Di Gesù, V., Lo Bosco, G., & Tegolo, D. (2003). Distributed image retrieval on Daisy. In 2003 IEEE International Workshop on Computer Architectures for Machine Perception (pp. 81-85). The Institute of Electrical and Electronics Engineers, IEEE [10.1109/CAMP.2003.1598151].
Lo Bosco, G. (2001). A genetic algorithm for image segmentation. In E. Ardizzone, & V. Di Gesu (eds), Proceedings - 11th International Conference on Image Analysis and Processing, ICIAP 2001 (pp. 262-266). The Institute of Electrical and Electronics Engineers, IEEE [10.1109/ICIAP.2001.957019].
Di Gesù, V., Lo Bosco, G., & Tegolo, D. (2001). Experiments on Concurrent Artificial Environment. In V. Cantoni, V. Di Gesù, A. Setti, & D. Tegolo (eds), Human and machine perception, Thinking, Deciding, and Acting (pp. 123-130). Springer, Boston, MA.
Di Gesù, V., Lenzitti, B., Lo Bosco, G., & Tegolo, D. (2000). A distributed architecture for autonomous navigation of robots. In V. Cantoni, & C. Guerra (eds), Proceedings Fifth IEEE International Workshop on Computer Architectures for Machine Perception (pp. 190-194). IEEE computer society press.
Lenzitti, B., Lo Bosco, G., & Tegolo, D. (1998). An Integrated Environment for Dynamic Processes in distributed Image Analysis System. In XXI convention MIPRO ’98 (pp.14-21). ieee computer society.
Chella, A., Di Gesù, V., Gaglio, S., Gerardi, G., Infantino, I., Intravaia, D., et al. (1997). DAISY: a distributed architecture for intelligent SYstem. In Computer Architectures for Machine Perception, Proceedings (CAMP) (pp. 42-50).
Preprints
Amato, D., Lo Bosco, G., & Giancarlo, R. (2023) Neural Networks as Building Blocks For The Design of Efficient Learned Indexes (link to download)
Amato, D., Lo Bosco, G., & Giancarlo, R. (2022), Learned Sorted Table Search and Static Indexes in Small Model Space (Extended Abstract) (link to download)