A Survey of Deep Learning Approaches for Natural Language Processing Tasks

In recent years, deep learning has been a go-to method for solving difficult NLP problems. Deep learning models have attained state-of-the-art performance across a wide range of natural language processing applications, including text summarization, sentiment analysis, named entity identification, and language translation, by utilizing enormous neural network designs and massive volumes of training data. In this paper, we take a look at the most important deep learning methods and how they've been used for different natural language processing jobs. We go over the basics of neural network designs including CNNs, RNNs, and transformers, and we also go over some of the more recent developments, such as BERT and GPT-3. Our discussion of each method centers on its guiding principles, benefits, drawbacks, and significant NLP applications. To further illustrate the relative merits of various models, we also provide their comparative performance findings on industry-standard benchmark datasets. We also highlight some of the present difficulties and potential future avenues of study in deep learning applied to natural language processing. The purpose of this survey is to offer academics and practitioners in natural language processing a high-level perspective on how to make good use of deep learning in their respective fields

Perspectives of patients on outpatient parenteral antimicrobial therapy: Experiences and adherence

Background: Nonadherence to medication is a burden to the US health care system and is associated with poor clinical outcomes. Data on outpatient parenteral antimicrobial therapy (OPAT) treatment plan adherence are lacking. The purpose of this study is to determine the rate of nonadherence and factors associated with it. Methods: We surveyed patients discharged from a tertiary hospital on OPAT between February and August 2019 about their baseline characteristics, OPAT regimen, adherence, and experience with OPAT. Results: Sixty-five patients responded to the survey. The median age was 62 years, and 56% were male. The rate of reported nonadherence to intravenous (IV) antibiotics was 10%. Factors associated with nonadherence to IV antibiotics included younger age, household income of \u3c$20 Conclusions: Less frequent antibiotic dosing and better social support were associated with improved adherence to OPAT. In contrast, younger age, lower income, and lack of time were associated with nonadherence

ANALIZA KOLIZJI W RUCHU MIEJSKIM Z WYKORZYSTANIEM TECHNIK GŁĘBOKIEGO UCZENIA

Road accidents are concerningly increasing in Andhra Pradesh. In 2021, Andhra Pradesh experienced a 20 percent upsurge in road accidents. The state's unfortunate position of being ranked eighth in terms of fatalities, with 8,946 lives lost in 22,311 traffic accidents, underscores the urgent nature of the problem. The significant financial impact on the victims and their families stresses the necessity for effective actions to reduce road accidents. This study proposes a framework that collects accident data from regions, namely Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam, and Gandhinagar in Vijayawada (India) from 2019 to 2021. The dataset comprises over 12,000 records of accident data. Deep learning techniques are applied to classify the severity of road accidents into Fatal, Grievous, and Severe Injuries. The classification procedure leverages advanced neural network models, including the Multilayer Perceptron, Long-Short Term Memory, Recurrent Neural Network, and Gated Recurrent Unit. These models are trained on the collected data to accurately predict the severity of road accidents. The project study to make important contributions for suggesting proactive measures and policies to reduce the severity and frequency of road accidents in Andhra Pradesh.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

URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES

Road accidents are concerningly increasing in Andhra Pradesh. In 2021, Andhra Pradesh experienced a 20 percent upsurge in road accidents. The state's unfortunate position of being ranked eighth in terms of fatalities, with 8,946 lives lost in 22,311 traffic accidents, underscores the urgent nature of the problem. The significant financial impact on the victims and their families stresses the necessity for effective actions to reduce road accidents. This study proposes a framework that collects accident data from regions, namely Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam, and Gandhinagar in Vijayawada (India) from 2019 to 2021. The dataset comprises over 12,000 records of accident data. Deep learning techniques are applied to classify the severity of road accidents into Fatal, Grievous, and Severe Injuries. The classification procedure leverages advanced neural network models, including the Multilayer Perceptron, Long-Short Term Memory, Recurrent Neural Network, and Gated Recurrent Unit. These models are trained on the collected data to accurately predict the severity of road accidents. The project study to make important contributions for suggesting proactive measures and policies to reduce the severity and frequency of road accidents in Andhra Pradesh