1. Influences on and prevention of self-harm behavior among the most at-risk adolescents : study protocol for the SH-MARA prospective longitudinal cohort studyLana Sernec Podnar, Petra Tomažič, Anja Tomašević Kramer, Barbara Plemeniti Tololeski, Gorjan Tasevski, Žiga Rosenstein, Simona Klemenčič, Tadej Battelino, Blaž Vrhovšek, Tadej Lahovnik, Jernej Kovač, Carla Sharp, Barbara Jenko Bizjan, Sašo Karakatič, Maja Drobnič Radobuljac, 2025, original scientific article Abstract: Background Both suicidal and non-suicidal self-injuring behaviors (NSSI) are common during adolescence In Slovenia, adolescent suicide rates are high, making suicide the leading cause of death in the year 2022 in this age group. These behaviors are influenced by a complex interplay of environmental, psychological, and genetic factors. Previous research has identified risk and protective factors mainly for suicidal behavior in adults, a notable gap in understanding these factors in adolescents remains, especially for NSSI. Notably there is an important lack of effective clinical tools or psychometric assessment methods to reliably assess the risk for either suicidal or NSSI behaviors in acutely hospitalized adolescents. Methods and analysis The proposed study uses a mixed-method observational design consisting of a prospective longitudinal cohort component involving adolescents hospitalized for high risk of DSH, and a cross-sectional comparison with a control group of healthy adolescents recruited from primary care settings. It is aimed at identifying genetic, psychosocial, and clinical factors associated with suicidal behaviors and NSSI in adolescents. The study group is recruited from adolescents aged 12–19, admitted to the Intensive Child and Adolescent Psychiatry Unit in Ljubljana due to severe self-harm risk. Exclusion criteria include involuntary treatment, acute psychotic disorders, intellectual disability, severe physical or central nervous system illnesses and acute intoxication. The control group comprises adolescents of comparable age, recruited through regular scheduled health check-ups in Slovenia. Exclusion criteria include suicidality, severe mental disorder, a history of self-harm behavior in a first-degree relative, intellectual disability, severe physical or central nervous system illnesses and acute intoxication. Enrollment runs from February 1, 2023, to December 31, 2025. Participation is voluntary, requiring parental or guardian consent for those 14 or younger Keywords: adolescents, deliberate self-harm, non-suicidal self-injury, suicidal behavior, intensive psychiatry, personality disorder, traumatic experience, genetics, epigenetics Published in DKUM: 17.10.2025; Views: 0; Downloads: 2
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2. Gradnja uravnoteženih evolucijskih klasifikacijskih dreves : magistrsko deloTadej Lahovnik, 2024, master's thesis Abstract: Uspešnost odločitvenih dreves temelji na predpostavki, da učni podatki za vsak razred vključujejo enako količino informacij. Pri nesorazmerni porazdelitvi razredov so klasifikatorji pristransko usmerjeni k večinskim razredom. Zaradi majhnega števila vzorcev manjšinskih razredov klasifikatorji niso zmožni ustreznega usvajanja znanja, kar vodi do slabšega posploševanja in prekomernega prileganja. V okviru zaključnega dela smo razvili več algoritmov za gradnjo uravnoteženih evolucijskih dreves, ki se osredotočajo na reševanje izzivov, povezanih z nesorazmerno porazdelitvijo razredov. Rezultati eksperimenta kažejo, da uravnoteženost evolucijskih dreves ne prispeva k izboljšanju klasifikacije v primerjavi s tradicionalnimi metodami. Keywords: evolucijski algoritem, odločitvena drevesa, klasifikacija, neuravnoteženi podatki Published in DKUM: 06.02.2025; Views: 0; Downloads: 86
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3. Klasifikacija glasbenega žanra glede na spektrogram zvočnega posnetka : diplomsko deloTadej Lahovnik, 2022, undergraduate thesis Abstract: V diplomskem delu smo se poglobili v izdelavo različnih tipov spektrogramov in klasifikacijo slik z uporabo konvolucijskih nevronskih mrež. Zanimalo nas je, ali je možno zanesljivo napovedati žanr zvočnega posnetka glede na spektrogram, ki mu pripada.
Tekom razvoja smo ustvarili tri različne tipe spektrogramov. Za vsak tip smo ustvarili ločen klasifikacijski model, nato pa smo iz vseh treh modelov sestavili klasifikacijski ansambel. Tako smo dobili najbolj zanesljive rezultate. Klasifikacijo smo nato ovrednotili s številnimi metrikami, kjer nas je najbolj zanimala sama točnost klasifikacije. Iz matrike zmede smo izčrpali najpogostejše napake pri klasifikaciji. Keywords: klasifikacija, spektrogram, strojno učenje, nevronske mreže, glasbeni
žanr Published in DKUM: 20.10.2022; Views: 3264; Downloads: 82
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