scholarly journals An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2811
Author(s):  
Waseem Ullah ◽  
Amin Ullah ◽  
Tanveer Hussain ◽  
Zulfiqar Ahmad Khan ◽  
Sung Wook Baik
Keyword(s):  
Time Complexity ◽  
Short Term Memory ◽  
Smart Cities ◽  
Vital Role ◽  
Training Data ◽  
Surveillance Videos ◽  
Video Frames ◽  
Proposed Model ◽  
Lstm Network ◽  
Increase In Accuracy

Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model’s effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively.

scholarly journals Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNN-LSTM Framework

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1804
Author(s):  
Wentai Lei ◽  
Jiabin Luo ◽  
Feifei Hou ◽  
Long Xu ◽  
Ruiqing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Vital Role ◽  
Short Term ◽  
Term Memory ◽  
Region Detection ◽  
Field Environment ◽  
Long Short Term Memory ◽  
Lstm Network

Ground penetrating radar (GPR), as a non-invasive instrument, has been widely used in the civil field. The interpretation of GPR data plays a vital role in underground infrastructures to transfer raw data to the interested information, such as diameter. However, the diameter identification of objects in GPR B-scans is a tedious and labor-intensive task, which limits the further application in the field environment. The paper proposes a deep learning-based scheme to solve the issue. First, an adaptive target region detection (ATRD) algorithm is proposed to extract the regions from B-scans that contain hyperbolic signatures. Then, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework is developed that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to extract hyperbola region features. It transfers the task of diameter identification into a task of hyperbola region classification. Experimental results conducted on both simulated and field datasets demonstrate that the proposed scheme has a promising performance for diameter identification. The CNN-LSTM framework achieves an accuracy of 99.5% on simulated datasets and 92.5% on field datasets.

scholarly journals A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model

2020 ◽  
Vol 12 (10) ◽  
pp. 4107
Author(s):  
Wafa Shafqat ◽  
Yung-Cheol Byun
Keyword(s):  
Recommendation System ◽  
Short Term Memory ◽  
Contextual Information ◽  
Optimal Size ◽  
Effective Parameters ◽  
Travel History ◽  
Proposed Model ◽  
Processing Information ◽  
Lstm Network ◽  
Gated Recurrent Unit

The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations.

scholarly journals Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network

2019 ◽  
Vol 8 (9) ◽  
pp. 366 ◽  
Author(s):  
Yong Han ◽  
Cheng Wang ◽  
Yibin Ren ◽  
Shukang Wang ◽  
Huangcheng Zheng ◽  
...  
Keyword(s):  
High Efficiency ◽  
Short Term Memory ◽  
Spatial Scales ◽  
Optimization Procedure ◽  
Stochastic Gradient Descent ◽  
Short Term ◽  
Passenger Flow ◽  
Proposed Model ◽  
Gradient Descent Algorithm ◽  
Lstm Network

The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In recent years, an increasing number of researchers have sought to apply the LSTM model to passenger flow prediction. However, few of them pay attention to the optimization procedure during model training. In this article, we propose a hybrid, optimized LSTM network based on Nesterov accelerated adaptive moment estimation (Nadam) and the stochastic gradient descent algorithm (SGD). This method trains the model with high efficiency and accuracy, solving the problems of inefficient training and misconvergence that exist in complex models. We employ a hybrid optimized LSTM network to predict the actual passenger flow in Qingdao, China and compare the prediction results with those obtained by non-hybrid LSTM models and conventional methods. In particular, the proposed model brings about a 4%–20% extra performance improvements compared with those of non-hybrid LSTM models. We have also tried combinations of other optimization algorithms and applications in different models, finding that optimizing LSTM by switching Nadam to SGD is the best choice. The sensitivity of the model to its parameters is also explored, which provides guidance for applying this model to bus passenger flow data modelling. The good performance of the proposed model in different temporal and spatial scales shows that it is more robust and effective, which can provide insightful support and guidance for dynamic bus scheduling and regional coordination scheduling.

scholarly journals Long Short-Term Memory with Dynamic Skip Connections

2019 ◽  
Vol 33 ◽  
pp. 6481-6488 ◽  
Author(s):  
Tao Gui ◽  
Qi Zhang ◽  
Lujun Zhao ◽  
Yaosong Lin ◽  
Minlong Peng ◽  
...  
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Training Data ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Transition Functions ◽  
Proposed Model ◽  
Long Short Term Memory

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.

scholarly journals Gaze Tracking Based on Concatenating Spatial-Temporal Features

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 545
Author(s):  
Bor-Jiunn Hwang ◽  
Hui-Hui Chen ◽  
Chaur-Heh Hsieh ◽  
Deng-Yu Huang
Keyword(s):  
Short Term Memory ◽  
Image Data ◽  
Training Model ◽  
Point Estimation ◽  
Training Data ◽  
Gaze Tracking ◽  
Spatial Features ◽  
Temporal Features ◽  
Lstm Network ◽  
The Impact

Based on experimental observations, there is a correlation between time and consecutive gaze positions in visual behaviors. Previous studies on gaze point estimation usually use images as the input for model trainings without taking into account the sequence relationship between image data. In addition to the spatial features, the temporal features are considered to improve the accuracy in this paper by using videos instead of images as the input data. To be able to capture spatial and temporal features at the same time, the convolutional neural network (CNN) and long short-term memory (LSTM) network are introduced to build a training model. In this way, CNN is used to extract the spatial features, and LSTM correlates temporal features. This paper presents a CNN Concatenating LSTM network (CCLN) that concatenates spatial and temporal features to improve the performance of gaze estimation in the case of time-series videos as the input training data. In addition, the proposed model can be optimized by exploring the numbers of LSTM layers, the influence of batch normalization (BN) and global average pooling layer (GAP) on CCLN. It is generally believed that larger amounts of training data will lead to better models. To provide data for training and prediction, we propose a method for constructing datasets of video for gaze point estimation. The issues are studied, including the effectiveness of different commonly used general models and the impact of transfer learning. Through exhaustive evaluation, it has been proved that the proposed method achieves a better prediction accuracy than the existing CNN-based methods. Finally, 93.1% of the best model and 92.6% of the general model MobileNet are obtained.

scholarly journals Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

2021 ◽  
Author(s):  
Farshid Rahmani ◽  
Chaopeng Shen ◽  
Samantha Oliver ◽  
Kathryn Lawson ◽  
Alison Appling
Keyword(s):  
Short Term Memory ◽  
Network Models ◽  
Stream Temperature ◽  
Data Availability ◽  
Training Data ◽  
Training Dataset ◽  
Learning Approaches ◽  
Input Selection ◽  
Daily Sampling ◽  
Lstm Network

Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for simulating stream temperature (Ts, temperature measured in rivers), among other hydrological variables. However, spatial extrapolation is a well-known challenge to modeling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitored or dammed basins. Here we compiled a new benchmark dataset consisting of >400 basins for across the contiguous United States in different data availability groups (DAG, meaning the daily sampling frequency) with or without major dams and study how to assemble suitable training datasets for predictions in monitored or unmonitored situations. For temporal generalization, CONUS-median best root-mean-square error (RMSE) values for sites with extensive (99%), intermediate (60%), scarce (10%) and absent (0%, unmonitored) data for training were 0.75, 0.83, 0.88, and 1.59°C, representing the state of the art. For prediction in unmonitored basins (PUB), LSTM’s results surpassed those reported in the literature. Even for unmonitored basins with major reservoirs, we obtained a median RMSE of 1.492°C and an R2 of 0.966. The most suitable training set was the matching DAG that the basin could be grouped into, e.g., the 60% DAG for a basin with 61% data availability. However, for PUB, a training dataset including all basins with data is preferred. An input-selection ensemble moderately mitigated attribute overfitting. Our results suggest there are influential latent processes not sufficiently described by the inputs (e.g., geology, wetland covers), but temporal fluctuations are well predictable, and LSTM appears to be the more accurate Ts modeling tool when sufficient training data are available.

Fog Removal Algorithms for Real-Time Video Footage in Smart Cities for Safe Driving

2019 ◽  
pp. 55-86
Author(s):  
Neetu Sood ◽  
Indu Saini ◽  
Tarannum Awasthi ◽  
Milin Kaur Saini ◽  
Parul Bhoriwal ◽  
...  
Keyword(s):  
Time Complexity ◽  
Smart Cities ◽  
Histogram Equalization ◽  
Dark Channel Prior ◽  
Safe Driving ◽  
Video Footage ◽  
Video Frames ◽  
Dark Channel ◽  
Retinex Algorithm ◽  
Before And After

In this chapter, different approaches are presented for removal of fog from video footage taken in moving cars. The methodology uses different approaches, namely dark channel prior, contrast limited adaptive histogram equalization (CLAHE), the combination of two approaches (dark channel prior and CLAHE), and RETINEX algorithm combined with DWT. The algorithms are implemented in MATLAB R2015a. Moreover, the algorithms are compared based on their computational complexity and a visibility metric which is used for computing the CNR of video frames before and after the application of the algorithm. The chapter discusses which algorithm would provide better performance during night fog and daylight fog. Finally, the safe speed of the driver is calculated based on the time complexity of the algorithm used.

On the Use of Generative Deep Learning Approaches for Generating Hidden Test Scripts

2021 ◽  
Vol 31 (10) ◽  
pp. 1447-1468
Author(s):  
Mert Oz ◽  
Caner Kaya ◽  
Erdi Olmezogullari ◽  
Mehmet S. Aktas
Keyword(s):  
Web Application ◽  
Web Applications ◽  
Short Term Memory ◽  
Learning Approaches ◽  
Generation Task ◽  
Clickstream Data ◽  
Test Script ◽  
Proposed Model ◽  
Action Sequences ◽  
Lstm Network

With the advent of web 2.0, web application architectures have been evolved, and their complexity has grown enormously. Due to the complexity, testing of web applications is getting time-consuming and intensive process. In today’s web applications, users can achieve the same goal by performing different actions. To ensure that the entire system is safe and robust, developers try to test all possible user action sequences in the testing phase. Since the space of all the possibilities is enormous, covering all user action sequences can be impossible. To automate the test script generation task and reduce the space of the possible user action sequences, we propose a novel method based on long short-term memory (LSTM) network for generating test scripts from user clickstream data. The experiment results clearly show that generated hidden test sequences are user-like sequences, and the process of generating test scripts with the proposed model is less time-consuming than writing them manually.

scholarly journals Forecasting the spread of COVID-19 using LSTM network

2021 ◽  
Vol 22 (S6) ◽  
Author(s):  
Shiu Kumar ◽  
Ronesh Sharma ◽  
Tatsuhiko Tsunoda ◽  
Thirumananseri Kumarevel ◽  
Alok Sharma
Keyword(s):  
Short Term Memory ◽  
Forecasting Model ◽  
Policy Makers ◽  
Business Partners ◽  
Global Pandemic ◽  
Proposed Model ◽  
Travel Restrictions ◽  
The Government ◽  
Lstm Network ◽  
Novel Coronavirus

Abstract Background The novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed. Results The results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand’s data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19. Conclusion The forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread of COVID-19.

scholarly journals Predicting COVID-19 cases using bidirectional LSTM on multivariate time series

2021 ◽  
Author(s):  
Ahmed Ben Said ◽  
Abdelkarim Erradi ◽  
Hussein Ahmed Aly ◽  
Abdelmonem Mohamed
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Health Sector ◽  
Cumulative Number ◽  
Disease Propagation ◽  
Proposed Model ◽  
Bidirectional Lstm ◽  
Lstm Network

AbstractTo assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.

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