scholarly journals Teaching Design of Online Ideological and Political Course Based on Deep Learning Model Evaluation

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Lijun Qiao
Keyword(s):  
Deep Learning ◽  
Online Teaching ◽  
Evaluation Research ◽  
Real Life ◽  
Learning Model ◽  
Semantic Features ◽  
Knowledge Mapping ◽  
Teaching Design ◽  
Relationship Model ◽  
High Level

In practical terms, teachers are supported to use more straightforward teaching methods, such as creating real-life contextual problems, to help students develop deep learning skills. In this paper, using Bayesian theory and Bayesian classifier research methods, a machine learning model was constructed using Python to establish the correspondence between online teaching of civics and high-level semantic features and to achieve computer learning through text and teaching design evaluation research that can identify high-frequency knowledge points. The inter-relationship model knowledge mapping, the accuracy is 90%, and the continuous knowledge update help to improve the model accuracy.

scholarly journals Strategi Guru Dalam Menerapkan Pembelajaran Hots Menggunakan Model Problem Based Learning

2020 ◽  
Vol 2 (2) ◽  
pp. 27-34
Author(s):  
Ummi Inayati
Keyword(s):  
Data Collection ◽  
Learning Style ◽  
Real Life ◽  
Problem Based Learning ◽  
Thinking Skills ◽  
Learning Model ◽  
Class Iii ◽  
Key Informant ◽  
Collection Data ◽  
High Level

Education in Indonesia is currently undergoing a significant improvement. Particularly in welcoming the revolution era of the 4.0, which is required learning model with high level-thingking  or commonly known as HOTS (Higher Order Thinking Skills). In addition to thinking critically and creatively, problem solving is also included as thinking characters in high-level skills. Solving problems in the learning process trains learners to resolve problems in real life. In fact, not all teachers can simply apply it. Therefore, HOTS learning using the problem based learning model requires strategy for more effective and efficient study.  The research methods used in this study are qualitative descriptive, data collection techniques used by conducting observations, interviews in-depth and document research. The key informant in the study was a class III teacher at SDN Lengkong Bojonegoro, while the informant was a class III student. The data obtained is analyzed using interactive models (data collection, data presentation , reduction, drawing conclusions). The results of this study show that the teacher's strategy in implementing HOTS learning is a good problem based learning model. Visible from the indicators used through deep interview to the key informant and the informant. The obstacles experienced are differences in understanding, characteristics, learning style of students when learning, teachers are required to always be creative and innovative packing learning and the limitations of school facilities and infrastructure.  

scholarly journals Pedestrian Attributes Recognition in Surveillance Scenarios Using Multi-Task Lightweight Convolutional Neural Network

2019 ◽  
Vol 9 (19) ◽  
pp. 4182 ◽  
Author(s):  
Pu Yan ◽  
Li Zhuo ◽  
Jiafeng Li ◽  
Hui Zhang ◽  
Jing Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Cross Entropy ◽  
Semantic Features ◽  
Recognition Method ◽  
Relationship Model ◽  
High Level ◽  
Fully Connected ◽  
Video Structuring

Pedestrian attributes (such as gender, age, hairstyle, and clothing) can effectively represent the appearance of pedestrians. These are high-level semantic features that are robust to illumination, deformation, etc. Therefore, they can be widely used in person re-identification, video structuring analysis and other applications. In this paper, a pedestrian attributes recognition method for surveillance scenarios using a multi-task lightweight convolutional neural network is proposed. Firstly, the labels of the attributes for each pedestrian image are integrated into a label vector. Then, a multi-task lightweight Convolutional Neural Network (CNN) is designed, which consists of five convolutional layers, three pooling layers and two fully connected layers to extract the deep features of pedestrian images. Considering that the data distribution of the datasets is unbalanced, the loss function is improved based on the sigmoid cross-entropy, and the scale factor is added to balance the amount of various attributes data. Through training the network, the mapping relationship model between the deep features of pedestrian images and the integration label vector of their attributes is established, which can be used to predict each attribute of the pedestrian. The experiments were conducted on two public pedestrian attributes datasets in surveillance scenarios, namely PETA and RAP. The results show that, compared with the state-of-the-art pedestrian attributes recognition methods, the proposed method can achieve a superior accuracy by 91.88% on PETA and 87.44% on RAP respectively.

Deep Learning for Moving Object Detection and Tracking

2021 ◽  
pp. 136-163
Author(s):  
Kalirajan K. ◽  
Seethalakshmi V. ◽  
Venugopal D. ◽  
Balaji K.
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Model ◽  
Moving Object Detection ◽  
Moving Object ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
High Level ◽  
Human Faces ◽  
Deep Learning Model

Moving object detection and tracking is the process of identifying and locating the class objects such as people, vehicle, toy, and human faces in the video sequences more precisely without background disturbances. It is the first and foremost step in any kind of video analytics applications, and it is greatly influencing the high-level abstractions such as classification and tracking. Traditional methods are easily affected by the background disturbances and achieve poor results. With the advent of deep learning, it is possible to improve the results with high level features. The deep learning model helps to get more useful insights about the events in the real world. This chapter introduces the deep convolutional neural network and reviews the deep learning models used for moving object detection. This chapter also discusses the parameters involved and metrics used to assess the performance of moving object detection in deep learning model. Finally, the chapter is concluded with possible recommendations for the benefit of research community.

scholarly journals Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances

2018 ◽  
Author(s):  
Thao Le Minh ◽  
Nobuyuki Shimizu ◽  
Takashi Miyazaki ◽  
Koichi Shinoda
Keyword(s):  
Deep Learning ◽  
Human Computer Interaction ◽  
Intelligent Systems ◽  
Real Life ◽  
Smart Homes ◽  
Learning Model ◽  
Social Situations ◽  
Human Intention ◽  
Visual Scenes ◽  
Deep Learning Model

With the widespread use of intelligent systems, such as smart speakers, addressee recognition has become a concern in human-computer interaction, as more and more people expect such systems to understand complicated social scenes, including those outdoors, in cafeterias, and hospitals. Because previous studies typically focused only on pre-specified tasks with limited conversational situations such as controlling smart homes, we created a mock dataset called Addressee Recognition in Visual Scenes with Utterances (ARVSU) that contains a vast body of image variations in visual scenes with an annotated utterance and a corresponding addressee for each scenario. We also propose a multi-modal deep-learning-based model that takes different human cues, specifically eye gazes and transcripts of an utterance corpus, into account to predict the conversational addressee from a specific speaker's view in various real-life conversational scenarios. To the best of our knowledge, we are the first to introduce an end-to-end deep learning model that combines vision and transcripts of utterance for addressee recognition. As a result, our study suggests that future addressee recognition can reach the ability to understand human intention in many social situations previously unexplored, and our modality dataset is a first step in promoting research in this field.

scholarly journals Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Yiding Wang ◽  
Yuxin Qin ◽  
Jiali Cui
Keyword(s):  
Deep Learning ◽  
Original Model ◽  
Background Information ◽  
Semantic Features ◽  
False Detection Rate ◽  
False Detection ◽  
Detection Model ◽  
Backbone Network ◽  
High Level ◽  
Traditional Image

Counting the number of wheat ears in images under natural light is an important way to evaluate the crop yield, thus, it is of great significance to modern intelligent agriculture. However, the distribution of wheat ears is dense, so the occlusion and overlap problem appears in almost every wheat image. It is difficult for traditional image processing methods to solve occlusion problem due to the deficiency of high-level semantic features, while existing deep learning based counting methods did not solve the occlusion efficiently. This article proposes an improved EfficientDet-D0 object detection model for wheat ear counting, and focuses on solving occlusion. First, the transfer learning method is employed in the pre-training of the model backbone network to extract the high-level semantic features of wheat ears. Secondly, an image augmentation method Random-Cutout is proposed, in which some rectangles are selected and erased according to the number and size of the wheat ears in the images to simulate occlusion in real wheat images. Finally, convolutional block attention module (CBAM) is adopted into the EfficientDet-D0 model after the backbone, which makes the model refine the features, pay more attention to the wheat ears and suppress other useless background information. Extensive experiments are done by feeding the features to detection layer, showing that the counting accuracy of the improved EfficientDet-D0 model reaches 94%, which is about 2% higher than the original model, and false detection rate is 5.8%, which is the lowest among comparative methods.

Affective emotion classification using feature vector of image based on visual concepts

2020 ◽  
pp. 002072092093683
Author(s):  
D Tamil Priya ◽  
J Divya Udayan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recognition Performance ◽  
Learning Model ◽  
Experimental Result ◽  
Emotion Classification ◽  
Low Level ◽  
Visual Concepts ◽  
Deep Learning Network ◽  
High Level

Nowadays, deep learning technique becomes the most popular fast-growing machine learning method in an Artificial Neural Network. The Convolution Neural Network (CNN) is one of the deep learning architecture that has been applied in the field of image analysis and image classification. In this paper, we proposed a novel emotion learning model with a deep learning network. The aim of the learning model is to reduce the affective gap, that extracts the objects and background features of an image semantically, such as high-level and low-level features. These extracted features accompanied with few others and it is more effective in emotion prediction model based on visual concepts of image, that leads to better emotion recognition performance. For training and testing, the experiment is conducted on IAPS (International Affective Picture System) dataset, the Artistic Photos, and the Emotion-Image dataset. An experimental result shows that the proposed model combines visual-content and low-level features of the image that provides promising results for Affective Emotion Classification task.

scholarly journals Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network

2021 ◽  
Vol 11 (6) ◽  
pp. 2714
Author(s):  
Xue Zhang ◽  
Helmut Kuehnelt ◽  
Wim De Roeck
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Greenhouse Gas Emission ◽  
Smart Cities ◽  
Traffic Noise ◽  
Real Life ◽  
Learning Model ◽  
Traffic Data ◽  
Computation Efficiency

With the drastically increasing traffic in the last decades, crucial environmental problems have been caused, such as greenhouse gas emission and traffic noise pollution. These problems have adversely affected our life quality and health conditions. In this paper, modelling of traffic noise employing deep learning is investigated. The goal is to identify the best machine-learning model for predicting traffic noise from real-life traffic data with multivariate traffic features as input. An extensive study on recurrent neural network (RNN) is performed in this work for modelling time series traffic data, which was collected through an experimental campaign at an inner city roundabout, including both video traffic data and audio data. The preprocessing of the data, namely how to generate the appropriate input and output for deep learning model, is detailed in this paper. A selection of different architectures of RNN, such as many-to-one, many-to-many, encoder–decoder architectures, was investigated. Moreover, gated recurrent unit (GRU) and long short-term memory (LSTM) were further discussed. The results revealed that a multivariate bi-directional GRU model with many-to-many architecture achieved the best performance with both high accuracy and computation efficiency. The trained model could be promising for a future smart city concept; with the proposed model, real-time traffic noise predictions can be potentially feasible using only traffic data collected by different sensors in the city, thanks to the generated big data by smart cities. The forecast of excessive noise exposure can help the regulation and policy makers to make early decisions, in order to mitigate the noise level.

scholarly journals An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning

2021 ◽  
Vol 10 (5) ◽  
pp. 2442-2453
Author(s):  
Isaac Odun- Ayo ◽  
Williams Toro- Abasi ◽  
Marion Adebiyi ◽  
Oladapo Alagbe
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Web Applications ◽  
Learning Model ◽  
Project Work ◽  
Multi Layer Perceptron ◽  
Real Time Detection ◽  
High Level ◽  
Deep Learning Model ◽  
Cross Site

Cross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site scripting payloads and Benign user inputs extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious cross-site scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect cross-site scripting attacks using multi-layer perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models deep belief network, ensemble, and long short-term memory. A multi-layer perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting cross-site scripting attacks.

PELAKSANAAN MODEL PEMBELAJARAN KOOPERATIF TIPE STAD UNTUK MENINGKATKAN HASIL BELAJAR PPKn SISWA SMP NEGERI 3 TELUK KUANTAN

2018 ◽  
Vol 2 (5) ◽  
pp. 789
Author(s):  
Maria Yulianti
Keyword(s):  
Cooperative Learning ◽  
Initial Data ◽  
Student Learning ◽  
Learning Outcomes ◽  
Student Learning Outcomes ◽  
Learning Model ◽  
Learning Models ◽  
The Subject ◽  
High Level

The background of this study was the low student learning outcomes of PPKn, from 28 students who achievedthe completeness criteria at least only 11 students (39.29%). The low student learning outcomes are caused bythe high level of individuality between students so that the achievement of competence among studentsexperiences a very distant difference. Based on this, the researchers made improvements to student learningoutcomes through the application of STAD cooperative learning models. This research is a classroom actionresearch, with the subject of class VII of SMP Negeri 3 Teluk Kuantan. The data used in this study is PPKnlearning outcomes data. The results stated that after applying the STAD type cooperative learning model studentlearning outcomes had increased in the initial data the number of students who completed were 11 students, incycle I had an increase with the number of 18 students, and in cycle II the number of students who completedcontinued to increase by the number 22 student.

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