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Listar por tema "Random forests"

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    • Advanced block detection and quantification of fibrotic areas in microscopy images of obstructive nephropathy 

      Goudas, T.; Maglogiannis, I.; Chatziioannou, A. (2012)
      Obstructive nephropathy is not a rare disease and experts need a tool, which will provide them fast and accurate reproducible results for disease assessment. In this work we deal with the analysis of biopsy images for the ...

    • Assessing Durum Wheat Yield through Sentinel-2 Imagery: A Machine Learning Approach 

      Bebie M., Cavalaris C., Kyparissis A. (2022)
      Two modeling approaches for the estimation of durum wheat yield based on Sentinel-2 data are presented for 66 fields across three growing periods. In the first approach, a previously developed multiple linear regression ...

    • Estimating downlink throughput from end-user measurements in mobile broadband networks 

      Kousias K., Alay O., Argyriou A., Lutu A., Riegler M. (2019)
      In recent years, Downlink (DL)throughput estimation in Mobile Broadband (MBB)networks has gained immense popularity and it is expected to become a vital component of the upcoming fifth generation (5G)systems. Plentiful ...

    • Estimation of ground-level O3 using soft computing techniques: case study of Amritsar, Punjab State, India 

      Sihag P., Pandhiani S., Sangwan V., Kumar M., Angelaki A. (2022)
      Over the years, many organizations across the globe have conducted various studies pertaining to air pollution and its ill effects. The results of these studies substantially conclude that a plethora of people succumbs to ...

    • Estimation of the recharging rate of groundwater using random forest technique 

      Sihag P., Angelaki A., Chaplot B. (2020)
      Accurate knowledge of the recharging rate is essential for several groundwater-related studies and projects mainly in the water scarcity regions. In this study, a comparison between different methods of soft computing-based ...

    • Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology 

      Kokkotis C., Ntakolia C., Moustakidis S., Giakas G., Tsaopoulos D. (2022)
      Knee Osteoarthritis (ΚΟΑ) is a degenerative joint disease of the knee that results from the progressive loss of cartilage. Due to KOA’s multifactorial nature and the poor understanding of its pathophysiology, there is a ...

    • Heterogeneous data fusion and selection in high-volume molecular and imaging datasets 

      Moutselos, K.; Maglogiannis, I.; Chatziioannou, A. (2012)
      In this work, two disparate datasets, concerning the study of the same physiological type of cutaneous melanoma but derived from different donors, one of image (dermatoscopy) and the other of molecular (trascriptomic ...

    • Machine learning approaches for predicting health risk of cyanobacterial blooms in Northern European Lakes 

      Mellios N., Moe S.J., Laspidou C. (2020)
      Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail ...

    • Modelling of infiltration using artificial intelligence techniques in semi-arid Iran 

      Sihag P., Singh V.P., Angelaki A., Kumar V., Sepahvand A., Golia E. (2019)
      Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector ...

    • Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity 

      Sihag P., Mohsenzadeh Karimi S., Angelaki A. (2019)
      Hydraulic conductivity of soil reveals its influencing role in the studies related to management of surface and subsurface flow, e.g. irrigation and drainage projects, and solute mass transport models. Direct measurements ...

    • Skin lesion diagnosis from images using novel ensemble classification techniques 

      Maragoudakis, M.; Maglogiannis, I. (2010)
      Reduction of the error rate of melanoma diagnosis, a critical and very dangerous skin cancer that could be treated when early detected, is of major importance. Towards this direction, the present paper presents a novel ...

    • Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic 

      Van Lissa C.J., Stroebe W., vanDellen M.R., Leander N.P., Agostini M., Draws T., Grygoryshyn A., Gützgow B., Kreienkamp J., Vetter C.S., Abakoumkin G., Abdul Khaiyom J.H., Ahmedi V., Akkas H., Almenara C.A., Atta M., Bagci S.C., Basel S., Kida E.B., Bernardo A.B.I., Buttrick N.R., Chobthamkit P., Choi H.-S., Cristea M., Csaba S., Damnjanović K., Danyliuk I., Dash A., Di Santo D., Douglas K.M., Enea V., Faller D.G., Fitzsimons G.J., Gheorghiu A., Gómez Á., Hamaidia A., Han Q., Helmy M., Hudiyana J., Jeronimus B.F., Jiang D.-Y., Jovanović V., Kamenov, Kende A., Keng S.-L., Thanh Kieu T.T., Koc Y., Kovyazina K., Kozytska I., Krause J., Kruglanksi A.W., Kurapov A., Kutlaca M., Lantos N.A., Lemay E.P., Jr., Jaya Lesmana C.B., Louis W.R., Lueders A., Malik N.I., Martinez A.P., McCabe K.O., Mehulić J., Milla M.N., Mohammed I., Molinario E., Moyano M., Muhammad H., Mula S., Muluk H., Myroniuk S., Najafi R., Nisa C.F., Nyúl B., O'Keefe P.A., Olivas Osuna J.J., Osin E.N., Park J., Pica G., Pierro A., Rees J.H., Reitsema A.M., Resta E., Rullo M., Ryan M.K., Samekin A., Santtila P., Sasin E.M., Schumpe B.M., Selim H.A., Stanton M.V., Sultana S., Sutton R.M., Tseliou E., Utsugi A., Anne van Breen J., Van Veen K., Vázquez A., Wollast R., Wai-Lan Yeung V., Zand S., Žeželj I.L., Zheng B., Zick A., Zúñiga C., Bélanger J.J. (2022)
      Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors ...