Can machine learning predict the accuracy of preoperative planning for total hip arthroplasty, basing on patient-related factors? An explorative investigation on Supervised machine learning classification models.

Autor: Zampogna, B., Torre, G., Zampoli, A., Parisi, F., Ferrini, A., Shanmugasundaram, S., Franceschetti, E., Papalia, R.

Publikováno v: In Journal of Clinical Orthopaedics and Trauma June 2024 53

Exposure elevation and forest structure predict the abundance of saproxylic beetles’ communities in mountain managed beech forests

Autor: Parisi F, Mazziotta A, Vangi E, Tognetti R, Travaglini D, Marchetti M, D’Amico G, Francini S, Borghi C, Chirici G

Publikováno v: iForest - Biogeosciences and Forestry, Vol 16, Iss 1, Pp 155-164 (2023)

In the managed beech forests of Central Italy (Molise), the diversity of saproxylic species is potentially under threat by intensive management. To evaluate the impact of forestry on the biodiversity of these ecosystems, we analyzed the relationship

Externí odkaz: https://doaj.org/article/d035882ef6544f2ebfb71b36c8052b89

Association between sternal wound complications and 10-year mortality following coronary artery bypass grafting

Autor: Taggart, D., Ratnatunga, Westaby, S., Cook, J., Wallis, C., Wos, S., Jasinski, M., Deja, M., Widenka, K., Blach, A., Gocol, R., Hudziak, D., Zurek, P., Bachowski, R., Mrozek, R., Kargul, T., Domarardzki, W., Frackiewicz, J., Zamvar, V., Ezakadan, D., Buxton, B., Seevanayagam, S., Matalanis, G., Rosalion, A., Negri, J., Moten, S., Atkinson, V., Newcomb, A., Polidano, P., Pana, R., Gerbo, S., O'Keefe, P., von Oppell, U., Mehta, D., Azzu, A., Szafranek, A., Kulatilake, E., Evans, J., Martin, N., Banner, D., Trivedi, U., Forsyth, A., Hyde, J., Cohen, A., Lewis, M., Gardner, E., MacKenzie, A., Cooter, N., Joyce, E., Parker, J., Champney, F., Clark, S., Dark, J., Tocewicz, K., Pillay, T., Rowling, S., Adams-Hall, J., Bochenek, A., Cisowski, M., Bolkowski, M., Morawski, W., Guc, M., Krejca, M., Wilczynski, M., Duralek, A., Gerber, W., Skarysz, J., Shrestha, R., Swiech, W., Szmagala, X., Krzych, L., Pawlak, A., Kepa, K., Hasan, R., Keenan, D., Prendergast, B., Odom, N., McLaughlin, K., Cummings-Fosong, G., Mathew, C., Iles-Smith, H., Oomen, A., Desai, J., El-Gamel, A., John, L., Wendler, O., Andrews, M., Rance, K., Williams, R., Hogervorst, V., Gregory, J., Jessup, J., Knighton, A., Hoare, A., Ritchie, A., Choong, C., Nair, S., Sudarshan, C., Jenkins, D., Large, S., Barman, M., Dhital, K., Routledge, T., Rosengard, B., Munday, H., Rintoul, K., Jarrett, E., Lao-Sirieix, S., Wilkinson, A., Garner, L., Osmond, J., Holcombe, H., Cale, A., Griffin, S., Dickson, J., Spyt, T., Gershlick, A., Hickey, M., Sosnowski, A., Peek, G., Szostek, J., Hadjinikalaou, L., Logtens, E., Oakley, M., Leji, S., Gaer, J., Amrani, M., Dreyfus, G., Bahrami, T., de Robertis, F., Baig, K., Asimakopoulos, G., Vohra, H., Pai, V., Tadjkarimi, S., Soleimani, G., Stavri, G., Bull, H., Collappen, H., Sadowksi, J., Kapelak, B., Gaweda, B., Rudzinski, P., Stolinski, J., Konstanty-Kalandyk, J., Moraes, F., Moraes, C., Wanderley, J., Pepper, J., De Souza, A., Petrou, M., Trimlett, R., Morgan, T., Gavino, J., Wang, S.F., Chandrasekaran, V., Kanagasaby, R., Sarsam, M., Ryan, H., Billings, L., Ruddick, L., Achampong, A., Forster, E., Mohama, E., McDonnell, P., Pawlaczyk, R., Siondalski, P., Rogowski, J., Roszak, K., Jarmoszewicz, K., Jagielak, D., Gafka, S., Mannam, G., Naguboyin, Sajja, L. Rao, Dandu, B., Peddireddy, J., Adepu, A., Briffa, N., Braidley, P., Cooper, G., Allen, K., Sangha, G., Bridge, C., McMellon, H., Shaw, P., Casabona, R., Dato, G. Actis, Bardi, G., Del Ponte, S., Forsennati, F., Parisi, F., Punta, G., Flocco, R., Sansone, F., Zingarelli, E., Demartino, A., Dihmis, W., Kuduvali, M., Prince, C., Rogers, H., McQuade, L., Duran-Rosas, A., Anisimowicz, L., Bokszanski, M., Pawliszak, W., Kolakowski, J., Lau, G., Ogorzeja, W., Gumanska, I., Kulinski, P., Podesser, B., Trescher, K., Bernecker, O., Holzinger, C., Binder, K., Schor, I., Bergmann, P., Kassal, H., Dunkel, E., Motovova, B., Trehan, N., Meharwal, Z., Malhotra, R., Goel, M., Kumer, B., Bazaz, S., Bake, N., Singh, A., Mishka, Y., Gupta, R., Basumatary, S., Zembala, M., Szafron, B., Pacholewicz, J., Krason, M., Farmas, Wojarski, Zych, Szymanik, I., Kolwca, M., Mazur, W., Kurowicki, A., Zurek, S., Stacel, T., Jaworska, I., Gaudino, Mario, Audisio, Katia, Rahouma, Mohamed, Robinson, N. Bryce, Soletti, Giovanni J., Cancelli, Gianmarco, Masterson Creber, Ruth M., Gray, Alastair, Lees, Belinda, Gerry, Stephen, Benedetto, Umberto, Flather, Marcus, Taggart, David P.

Publikováno v: In The Journal of Thoracic and Cardiovascular Surgery August 2023 166(2):532-539

Maximum entropy approach to link prediction in bipartite networks

Autor: Baltakiene, M., Baltakys, K., Cardamone, D., Parisi, F., Radicioni, T., Torricelli, M., de Jeude, J. A. van Lidth, Saracco, F.

Within network analysis, the analytical maximum entropy framework has been very successful for different tasks as network reconstruction and filtering. In a recent paper, the same framework was used for link-prediction for monopartite networks: link

Externí odkaz: http://arxiv.org/abs/1805.04307

Automated location of steel truss bridge damage using machine learning and raw strain sensor data

Autor: Parisi, F., Mangini, A.M., Fanti, M.P., Adam, Jose M.

Publikováno v: In Automation in Construction June 2022 138