scholarly journals Challenges of Deep Learning for Crowd Analytics

2019 ◽  
Author(s):  
Muhammad Siraj
Keyword(s):  
Deep Learning ◽  
Large Datasets ◽  
Learning Models ◽  
Public Places ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Training Samples ◽  
Public Areas ◽  
Pros And Cons ◽  
And Training

In high population cities, the gatherings of large crowds in public places and public areas accelerate or jeopardize people safety and transportation, which is a key challenge to the researchers. Although much research has been carried out on crowd analytics, many of existing methods are problem-specific, i.e., methods learned from a specific scene cannot be properly adopted to other videos. Therefore, this presents weakness and the discovery of these researches, since additional training samples have to be found from diverse videos. This paper will investigate diverse scene crowd analytics with traditional and deep learning models. We will also consider pros and cons of these approaches. However, once general deep methods are investigated from large datasets, they can be consider to investigate different crowd videos and images. Therefore, it would be able to cope with the problem including to not limited to crowd density estimation, crowd people counting, and crowd event recognition. Deep learning models and approaches are required to have large datasets for training and testing. Many datasets are collected taking into account many different and various problems related to building crowd datasets, including manual annotations and increasing diversity of videos and images. In this paper, we will also propose many models of deep neural networks and training approaches to learn the feature modeling for crowd analytics.

scholarly journals Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES

2021 ◽  
Author(s):  
AkshatKumar Nigam ◽  
Robert Pollice ◽  
Mario Krenn ◽  
Gabriel dos Passos Gomes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Chemical Space ◽  
Generative Models ◽  
Inverse Design ◽  
Learning Models ◽  
Structure Modification ◽  
Design Models ◽  
Comparable Performance ◽  
And Training

Inverse design allows the design of molecules with desirable properties using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. We achieve comparable performance on typical benchmarks without any training. We demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. We anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wide adoption.

A Similarity-Based Object Classification Using Deep Neural Networks

2019 ◽  
pp. 197-219
Author(s):  
Parvathi R. ◽  
Pattabiraman V.
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Search Algorithm ◽  
Random Projection ◽  
Learning Networks ◽  
Learning Models ◽  
External Conditions ◽  
And Training

This chapter proposes a hybrid method for classification of the objects based on deep neural network and a similarity-based search algorithm. The objects are pre-processed with external conditions. After pre-processing and training different deep learning networks with the object dataset, the authors compare the results to find the best model to improve the accuracy of the results based on the features of object images extracted from the feature vector layer of a neural network. RPFOREST (random projection forest) model is used to predict the approximate nearest images. ResNet50, InceptionV3, InceptionV4, and DenseNet169 models are trained with this dataset. A proposal for adaptive finetuning of the deep learning models by determining the number of layers required for finetuning with the help of the RPForest model is given, and this experiment is conducted using the Xception model.

scholarly journals A comparison of deep learning models for end-to-end face-based video retrieval in unconstrained videos

2022 ◽  
Author(s):  
Gioele Ciaparrone ◽  
Leonardo Chiariglione ◽  
Roberto Tagliaferri
Keyword(s):  
Deep Learning ◽  
Video Retrieval ◽  
Large Datasets ◽  
Validation Dataset ◽  
Learning Models ◽  
Average Precision ◽  
Query Image ◽  
Shot Detection ◽  
Independent Test ◽  
End To End

AbstractFace-based video retrieval (FBVR) is the task of retrieving videos that containing the same face shown in the query image. In this article, we present the first end-to-end FBVR pipeline that is able to operate on large datasets of unconstrained, multi-shot, multi-person videos. We adapt an existing audiovisual recognition dataset to the task of FBVR and use it to evaluate our proposed pipeline. We compare a number of deep learning models for shot detection, face detection, and face feature extraction as part of our pipeline on a validation dataset made of more than 4000 videos. We obtain 97.25% mean average precision on an independent test set, composed of more than 1000 videos. The pipeline is able to extract features from videos at $$\sim $$ ∼ 7 times the real-time speed, and it is able to perform a query on thousands of videos in less than 0.5 s.

Negative emotion diffusion and intervention countermeasures of social networks based on deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 4935-4945
Author(s):  
Qiuyun Cheng ◽  
Yun Ke ◽  
Ahmed Abdelmouty
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Negative Emotion ◽  
Sentiment Classification ◽  
Classification Model ◽  
Learning Models ◽  
Data Set ◽  
Position Information ◽  
Processing Technologies ◽  
And Training

Aiming at the limitation of using only word features in traditional deep learning sentiment classification, this paper combines topic features with deep learning models to build a topic-fused deep learning sentiment classification model. The model can fuse topic features to obtain high-quality high-level text features. Experiments show that in binary sentiment classification, the highest classification accuracy of the model can reach more than 90%, which is higher than that of commonly used deep learning models. This paper focuses on the combination of deep neural networks and emerging text processing technologies, and improves and perfects them from two aspects of model architecture and training methods, and designs an efficient deep network sentiment analysis model. A CNN (Convolutional Neural Network) model based on polymorphism is proposed. The model constructs the CNN input matrix by combining the word vector information of the text, the emotion information of the words, and the position information of the words, and adjusts the importance of different feature information in the training process by means of weight control. The multi-objective sample data set is used to verify the effectiveness of the proposed model in the sentiment analysis task of related objects from the classification effect and training performance.

scholarly journals DGG: A Novel Framework for Crowd Gathering Detection

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 31
Author(s):  
Jianqiang Xu ◽  
Haoyu Zhao ◽  
Weidong Min ◽  
Yi Zou ◽  
Qiyan Fu
Keyword(s):  
Deep Learning ◽  
Local Area ◽  
Video Frame ◽  
Detection Accuracy ◽  
Learning Approaches ◽  
Counting Method ◽  
Crowd Counting ◽  
Stable Pattern ◽  
Crowd Density ◽  
Public Areas

Crowd gathering detection plays an important role in security supervision of public areas. Existing image-processing-based methods are not robust for complex scenes, and deep-learning-based methods for gathering detection mainly focus on the design of the network, which ignores the inner feature of the crowd gathering action. To alleviate such problems, this work proposes a novel framework Detection of Group Gathering (DGG) based on the crowd counting method using deep learning approaches and statistics to detect crowd gathering. The DGG mainly contains three parts, i.e., Detecting Candidate Frame of Gathering (DCFG), Gathering Area Detection (GAD), and Gathering Judgement (GJ). The DCFG is proposed to find the frame index in a video that has the maximum people number based on the crowd counting method. This frame means that the crowd has gathered and the specific gathering area will be detected next. The GAD detects the local area that has the maximum crowd density in a frame with a slide search box. The local area contains the inner feature of the gathering action and represents that the crowd gathering in this local area, which is denoted by grid coordinates in a video frame. Based on the detected results of the DCFG and the GAD, the GJ is proposed to analyze the statistical relationship between the local area and the global area to find the stable pattern for the crowd gathering action. Experiments based on benchmarks show that the proposed DGG has a robust representation of the gathering feature and a high detection accuracy. There is the potential that the DGG can be used in social security and smart city domains.

scholarly journals Multilayer Perceptron with Auto encoder enabled Deep Learning model for Recommender Systems

2020 ◽  
Vol 5 (2) ◽  
pp. 96-116
Author(s):  
subhashini narayan ◽  
Keyword(s):  
Deep Learning ◽  
Recommender Systems ◽  
Multilayer Perceptron ◽  
Recommendation System ◽  
Error Rates ◽  
Large Datasets ◽  
Modern World ◽  
Learning Models ◽  
Research Article ◽  
Deep Learning Model

In this modern world of ever-increasing one-click purchases, movie bookings, music, healthcare, fashion, the need for recommendations have increased the more. Google, Netflix, Spotify, Amazon and other tech giants use recommendations to customize and tailor their search engines to suit the user’s interests. Many of the existing systems are based on older algorithms which although have decent accuracies, require large training and testing datasets and with the emergence of deep learning, the accuracy of algorithms has further improved, and error rates have reduced due to the use of multiple layers. The need for large datasets has declined as well. This research article propose a recommendation system based on deep learning models such as multilayer perceptron that would provide a slightly more efficient and accurate recommendations.

scholarly journals Free annotated data for deep learning in microscopy? A hitchhiker’s guide

Photoniques ◽  
2020 ◽  
pp. 30-33
Author(s):  
Adrian Shajkofci ◽  
Michael Liebling
Keyword(s):  
Deep Learning ◽  
Large Datasets ◽  
Learning Models ◽  
Time Burden ◽  
Application Fields

In microscopy, the time burden and cost of acquiring and annotating large datasets that many deep learning models take as a prerequisite, often appears to make these methods impractical. Can this requirement for annotated data be relaxed? Is it possible to borrow the knowledge gathered from datasets in other application fields and leverage it for microscopy? Here, we aim to provide an overview of methods that have recently emerged to successfully train learning-based methods in bio-microscopy.

scholarly journals Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES

2021 ◽  
Author(s):  
AkshatKumar Nigam ◽  
Robert Pollice ◽  
Mario Krenn ◽  
Gabriel dos Passos Gomes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Chemical Space ◽  
Generative Models ◽  
Inverse Design ◽  
Learning Models ◽  
Structure Modification ◽  
Design Models ◽  
Comparable Performance ◽  
And Training

Inverse design allows the design of molecules with desirable properties using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. We achieve comparable performance on typical benchmarks without any training. We demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. We anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wide adoption.

A new weighted MSE loss for wind speed forecasting based on deep learning models

2020 ◽  
Author(s):  
Xi Chen ◽  
Ruyi Yu ◽  
Sajid Ullah ◽  
Dianming Wu ◽  
Min Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Wind Speed ◽  
Loss Function ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Wind Speed Forecasting ◽  
Training Samples ◽  
Forecasting Performance

<p>Wind speed forecasting is very important for a lot of real-life applications, especially for controlling and monitoring of wind power plants. Owing to the non-linearity of wind speed time series, it is hard to improve the accuracy of runoff forecasting, especially several days ahead. In order to improve the forecasting performance, many forecasting models have been proposed. Recently, deep learning models have been paid great attention, since they excel the conventional machine learning models. The majority of existing deep learning models take the mean squared error (MSE) loss as the loss function for forecasting. MSE loss is linear. Consequently, it hinders further improvement of forecasting performance over nonlinear wind speed time series data.   <br> <br>In this work, we propose a new weighted MSE loss function for wind speed forecasting based on deep learning. As is well known, the training procedure is dominated by easy-training samples in applications. The domination will cause the ineffectiveness and inefficiency of computation. In the new weighted MSE loss function, loss weights of samples can be automatically reduced, according to the contribution of easy-training samples. Thus, the total loss mainly focuses on hard-training samples. To verify the new loss function, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have been used as base models. <br> <br>A number of experiments have been carried out by using open wind speed time series data collected from China and Unites states to demonstrate the effectiveness of the new loss function with three popular models. The performances of the models have been evaluated through the statistical error measures, such as Mean Absolute Error (MAE). MAE of the proposed weighted MSE loss are at most 55% lower than traditional MSE loss. The experimental results indicate that the new weighted loss function can outperform the popular MSE loss function in wind speed forecasting. </p>

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