ANALIZA KOLIZJI W RUCHU MIEJSKIM Z WYKORZYSTANIEM TECHNIK GŁĘBOKIEGO UCZENIA
Mummaneni Sobhana
sobhana@vrsiddhartha.ac.inVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (Indie)
http://orcid.org/0000-0001-5938-5740
Nihitha Vemulapalli
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (Indie)
http://orcid.org/0009-0007-8626-0012
Gnana Siva Sai Venkatesh Mendu
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (Indie)
http://orcid.org/0009-0004-6406-000X
Naga Deepika Ginjupalli
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (Indie)
http://orcid.org/0009-0005-0436-0008
Pragathi Dodda
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (Indie)
http://orcid.org/0009-0003-4879-6298
Rayanoothala Bala Venkata Subramanyam
National Institute of Technology Warangal, Department of CSE (Indie)
http://orcid.org/0009-0005-8907-1984
Abstrakt
Liczba wypadków drogowych w Andhra Pradesh niepokojąco rośnie. W 2021 r. stan Andhra Pradesh odnotował 20% wzrost liczby wypadków drogowych. Niefortunna pozycja stanu, który zajmuje ósme miejsce pod względem liczby ofiar śmiertelnych, z 8 946 ofiarami śmiertelnymi w 22 311 wypadkach drogowych, podkreśla pilny charakter problemu. Znaczący wymiar finansowy dla ofiar i ich rodziny podkreśla konieczność podjęcia skutecznych działań w celu ograniczenia liczby wypadków drogowych. W niniejszym badaniu zaproponowano system gromadzenia danych o wypadkach z regionów Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam i Gandhinagar w Vijayawada (India) w latach 2019–2021. Zbiór danych obejmuje ponad 12 000 rekordów danych o wypadkach. Techniki głębokiego uczenia są stosowane do klasyfikowania wagi wypadków drogowych na śmiertelne, poważne i ciężkie obrażenia. Procedura klasyfikacji wykorzystuje zaawansowane modele sieci neuronowych, w tym wielowarstwowy perceptron, pamięć długoterminową i krótkoterminową, rekurencyjną sieć neuronową i Gated Recurrent Unit. Modele te są trenowane na zebranych danych w celu dokładnego przewidywania wagi wypadków drogowych. Projekt ma wnieść istotny wkład w sugerowanie proaktywnych środków i polityk mających na celu zmniejszenie dotkliwości i częstotliwości wypadków drogowych w Andhra Pradesh.
Słowa kluczowe:
klasyfikacja, gated recurrent unit, pamięć długotrwała i krótkotrwała, perceptron wielowarstwowy, rekurencyjna sieć neuronowa, wypadki drogoweBibliografia
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Autorzy
Mummaneni Sobhanasobhana@vrsiddhartha.ac.in
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering Indie
http://orcid.org/0000-0001-5938-5740
Autorzy
Nihitha VemulapalliVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering Indie
http://orcid.org/0009-0007-8626-0012
Autorzy
Gnana Siva Sai Venkatesh MenduVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering Indie
http://orcid.org/0009-0004-6406-000X
Autorzy
Naga Deepika GinjupalliVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering Indie
http://orcid.org/0009-0005-0436-0008
Autorzy
Pragathi DoddaVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering Indie
http://orcid.org/0009-0003-4879-6298
Autorzy
Rayanoothala Bala Venkata SubramanyamNational Institute of Technology Warangal, Department of CSE Indie
http://orcid.org/0009-0005-8907-1984
Statystyki
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Licencja
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Inne teksty tego samego autora
- Sobhana Mummaneni, Pragathi Dodda, Naga Deepika Ginjupalli, INSPIROWANE KOJOTAMI PODEJŚCIE DO PRZEWIDYWANIA TOCZNIA RUMIENIOWATEGO UKŁADOWEGO Z WYKORZYSTANIEM SIECI NEURONOWYCH , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Tom 14 Nr 2 (2024)