Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Deep Learning." wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-8 z 8
    Tytuł:
    Navigation strategy for mobile robot based on computer vision and YOLOv5 network in the unknown environment
    Autorzy:
    Bui, Thanh-Lam
    Tran, Ngoc-Tien
    Powiązania:
    https://bibliotekanauki.pl/articles/30148249.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    mobile robot
    navigation
    deep learning
    computer vision
    Opis:
    The capacity to navigate effectively in complex environments is a crucial prerequisite for mobile robots. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. When the light illumination increases from 300 lux to 1000 lux, the reliability of the recognition model on different objects also improves, from about 75% to 98%, respectively. The findings of this study offer promising directions for future breakthroughs in mobile robot navigation.
    Źródło:
    Applied Computer Science; 2023, 19, 2; 82-95
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Tomato disease detection model based on densenet and transfer learning
    Autorzy:
    Bakr, Mahmoud
    Abdel-Gaber, Sayed
    Nasr, Mona
    Hazman, Maryam
    Powiązania:
    https://bibliotekanauki.pl/articles/2097440.pdf
    Data publikacji:
    2022
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    leaf disease detection
    convolutional neural network
    deep learning
    transfer learning
    Opis:
    Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.
    Źródło:
    Applied Computer Science; 2022, 18, 2; 56--70
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Classification of Parkinsons disease in brain MRI images using Deep Residual Convolutional Neural Network (DRCNN)
    Autorzy:
    Praneeth, Puppala
    Sathvika, Majety
    Kommareddy, Vivek
    Sarath, Madala
    Mallela, Saran
    Vani, K. Suvarna
    Chkrabarti, Prasun
    Powiązania:
    https://bibliotekanauki.pl/articles/30148251.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    Parkinson’s disease
    Deep Residual Convolutional Neural Network
    deep learning
    health control
    Opis:
    In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, the authors propose a technique to classify Parkinson’s disease by MRI brain images. Initially, the input data is normalized using the min-max normalization method, and then noise is removed from the input images using a median filter. The Binary Dragonfly algorithm is then used to select features. In addition, the Dense-UNet technique is used to segment the diseased part from brain MRI images. The disease is then classified as Parkinson's disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with the Enhanced Whale Optimization Algorithm (EWOA) to achieve better classification accuracy. In this work, the Parkinson's Progression Marker Initiative (PPMI) public dataset for Parkinson's MRI images is used. Indicators of accuracy, sensitivity, specificity and precision are used with manually collected data to evaluate the effectiveness of the proposed methodology.
    Źródło:
    Applied Computer Science; 2023, 19, 2; 125-146
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Career track prediction using deep learning model based on discrete series of quantitative classification
    Autorzy:
    Hernandez, Rowell
    Atienza, Robert
    Powiązania:
    https://bibliotekanauki.pl/articles/1956033.pdf
    Data publikacji:
    2021
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    track prediction
    deep learning
    education
    przewidywanie torów
    głębokie uczenie
    edukacja
    Opis:
    In this paper, a career track recommender system was proposed using Deep Neural Network model. This study aims to assist guidance counselors in guiding their students in the selection of a suitable career track. It is because a lot of Junior High school students experienced track uncertainty and there are instances of shifting to another program after learning they are not suited for the chosen track or course in college. In dealing with the selection of the best student attributes that will help in the creation of the predictive model, the feature engineering technique is used to remove the irrelevant features that can affect the performance of the DNN model. The study covers 1500 students from the first to the third batch of the K-12 curriculum, and their grades from 11 subjects, sex, age, number of siblings, parent’s income, and academic strand were used as attributes to predict their academic strand in Senior High School. The efficiency and accuracy of the algorithm depend upon the correctness and quality of the collected student’s data. The result of the study shows that the DNN algorithm performs reasonably well in predicting the academic strand of students with a predic-tion accuracy of 83.11%. Also, the work of guidance counselors became more efficient in handling students’ concerns just by using the proposed system. It is concluded that the recommender system serves as a decision tool for counselors in guiding their stu-dents to determine which Senior High School track is suitable for students with the utilization of the DNN model.
    Źródło:
    Applied Computer Science; 2021, 17, 4; 55-74
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A lightweight multi-person pose estimation scheme based on Jetson Nano
    Autorzy:
    Liu, Lei
    Blancaflor, Eric B.
    Abisado, Mideth
    Powiązania:
    https://bibliotekanauki.pl/articles/30148243.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    human pose estimation
    lightweight model
    Edge AI
    deep learning
    computer vision
    Opis:
    As the basic technology of human action recognition, pose estimation is attracting more and more researchers' attention, while edge application scenarios pose a higher challenge. This paper proposes a lightweight multi-person pose estimation scheme to meet the needs of real-time human action recognition on the edge end. This scheme uses AlphaPose to extract human skeleton nodes, and adds ResNet and Dense Upsampling Revolution to improve its accuracy. Meanwhile, we use YOLO to enhance AlphaPose’s support for multi-person pose estimation, and optimize the proposed model with TensorRT. In addition, this paper sets Jetson Nano as the Edge AI deployment device of the proposed model and successfully realizes the model migration to the edge end. The experimental results show that the speed of the optimized object detection model can reach 20 FPS, and the optimized multi-person pose estimation model can reach 10 FPS. With the image resolution of 320×240, the model’s accuracy is 73.2%, which can meet the real-time requirements. In short, our scheme can provide a basis for lightweight multi-person action recognition scheme on the edge end.
    Źródło:
    Applied Computer Science; 2023, 19, 1; 1-14
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Unsupervised dynamic topic model for extracting adverse drug reaction from health forums
    Autorzy:
    Eslami, Behnaz
    Motlagh, Mehdi Habibzadeh
    Rezaei, Zahra
    Eslami, Mohammad
    Amini, Mohammad Amin
    Powiązania:
    https://bibliotekanauki.pl/articles/117691.pdf
    Data publikacji:
    2020
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    Deep Learning
    topic modeling
    Text Mining
    ADR
    NMF
    analiza tekstu
    uczenie maszynowe
    modelowanie tematyczne
    Opis:
    The relationship between drug and its side effects has been outlined in two websites: Sider and WebMD. The aim of this study was to find the association between drug and its side effects. We compared the reports of typical users of a web site called: “Ask a patient” website with reported drug side effects in reference sites such as Sider and WebMD. In addition, the typical users’ comments on highly-commented drugs (Neurotic drugs, Anti-Pregnancy drugs and Gastrointestinal drugs) were analyzed, using deep learning method. To this end, typical users’ comments on drugs' side effects, during last decades, were collected from the website “Ask a patient”. Then, the data on drugs were classified based on deep learning model (HAN) and the drugs’ side effect. And the main topics of side effects for each group of drugs were identified and reported, through Sider and WebMD websites. Our model demonstrates its ability to accurately describe and label side effects in a temporal text corpus by a deep learning classifier which is shown to be an effective method to precisely discover the association between drugs and their side effects. Moreover, this model has the capability to immediately locate information in reference sites to recognize the side effect of new drugs, applicable for drug companies. This study suggests that the sensitivity of internet users and the diverse scientific findings are for the benefit of distinct detection of adverse effects of drugs, and deep learning would facilitate it.
    Źródło:
    Applied Computer Science; 2020, 16, 1; 41-59
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An overview of deep learning techniques for short-term electricity load forecasting
    Autorzy:
    Adewuyi, Saheed
    Aina, Segun
    Uzunuigbe, Moses
    Lawal, Aderonke
    Oluwaranti, Adeniran
    Powiązania:
    https://bibliotekanauki.pl/articles/117932.pdf
    Data publikacji:
    2019
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    Short-term Load Forecasting
    Deep Learning Architectures
    RNN
    LSTM
    CNN
    SAE
    prognozowanie obciążenia krótkoterminowego
    architektura głębokiego uczenia
    Opis:
    This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems.
    Źródło:
    Applied Computer Science; 2019, 15, 4; 75-92
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A deep learning model for electricity demand forecasting based on a tropical data
    Autorzy:
    Adewuyi, Saheed A.
    Aina, Segun
    Oluwaranti, Adeniran I.
    Powiązania:
    https://bibliotekanauki.pl/articles/118123.pdf
    Data publikacji:
    2020
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    Electricity Demand Forecasting
    STLF
    Deep Learning Techniques
    LSTM
    CNN
    MLP
    prognozowanie zapotrzebowania na energię elektryczną
    techniki głębokiego uczenia
    Opis:
    Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to computer vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load forecasting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem.
    Źródło:
    Applied Computer Science; 2020, 16, 1; 5-17
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies