scholarly journals EARLY ACTION PREDICTION USING VGG16 MODEL AND BIDIRECTIONAL LSTM

2021 ◽  
Vol 9 (1) ◽  
pp. 666-672
Author(s):  
Manju D, Dr. Seetha M, Dr. Sammulal P
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Early Stage ◽  
Early Prediction ◽  
Action Prediction ◽  
Automated Driving ◽  
Time Step ◽  
Driving System ◽  
Early Action

Action prediction plays a key function, where an expected action needs to be identified before the action is completely performed. Prediction means inferring a potential action until it occurs at its early stage. This paper emphasizes on early action prediction, to predict an action before it occurs. In real time scenarios, the early prediction can be very crucial and has many applications like automated driving system, healthcare, video surveillance and other scenarios where a proactive action is needed before the situation goes out of control. VGG16 model is used for the early action prediction which is a convolutional neural network with 16 layers depth. Besides its capability of classifying objects in the frames, the availability of model weights enhances its capability. The model weights are available freely and preferred to used in different applications or models. The VGG-16 model along with Bidirectional structure of Lstm enables the network to provide both backward and forward information at every time step. The results of the proposed approach increased observation ratio ranging from 0.1 to 1.0 compared with the accuracy of GAN model.

scholarly journals Prediction with Prevention of the Chronic Kidney Disease by Implementing the Convolutional Neural Network Algorithm

2021 ◽  
pp. 166-172
Author(s):  
Prof. Nilam Kadale ◽  
Pranav Hugar ◽  
Kiran Panchal ◽  
Kirtiraj Botre
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Kidney Diseases ◽  
Early Stage ◽  
Random Forest Classifier ◽  
Early Prediction ◽  
Neural Network Algorithm ◽  
Last Stage ◽  
Predicted Model ◽  
The Given

Around 12% of population suffers from kidney diseases whose symptoms are unknown to them until last stage. CKD is diagnosed if evidence of kidney damage has been present for more than 3 months. Approximate 75% patients are undiagnosed because of no early prediction. Improved prediction model with accurate rate to identify early stage can help to predict disease in early stages. In this paper by using two algorithms i.e., Convolutional neural network and and random forest classifier a predicted model is build whose output will be, if the given user has CKD or not.

scholarly journals Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

2021 ◽  
Vol 13 (4) ◽  
pp. 554
Author(s):  
A. A. Masrur Ahmed ◽  
Ravinesh C Deo ◽  
Nawin Raj ◽  
Afshin Ghahramani ◽  
Qi Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Climate Index ◽  
Climate Indices ◽  
High Temporal Resolution ◽  
Synoptic Scale ◽  
Time Step ◽  
Gated Recurrent Unit

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

scholarly journals The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1737
Author(s):  
Wooseop Lee ◽  
Min-Hee Kang ◽  
Jaein Song ◽  
Keeyeon Hwang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Processing Speed ◽  
Model Comparison ◽  
Distance Estimation ◽  
Visual Object ◽  
Suitable Model ◽  
Automated Vehicles ◽  
Automated Driving

As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types.

scholarly journals Automatic Handgun Detection with Deep Learning in Video Surveillance Images

2021 ◽  
Vol 11 (13) ◽  
pp. 6085
Author(s):  
Jesus Salido ◽  
Vanesa Lomas ◽  
Jesus Ruiz-Santaquiteria ◽  
Oscar Deniz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Automatic Detection ◽  
Public Spaces ◽  
Detection Methods ◽  
Training Dataset ◽  
Average Precision ◽  
Terrorist Acts

There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training.

Privacy Preserving in Video Surveillance Systems Using Regression Residual Convolutional Neural Network in Private and Public Places

2020 ◽  
Vol 12 (1) ◽  
pp. 39-55
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Security Policy ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Public Places ◽  
Private And Public ◽  
The One ◽  
Regression Residual

In recent years, surveillance video has become a familiar phenomenon because it gives us a feeling of greater security, but we are continuously filmed and our privacy is greatly affected. This work deals with the development of a private video surveillance system (PVSS) using regression residual convolutional neural network (RR-CNN) with the goal to propose a new security policy to ensure the privacy of no-dangerous person and prevent crime. The goal is to best meet the interests of all parties: the one who films and the one who is filmed.

scholarly journals A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Lian Zou ◽  
Shaode Yu ◽  
Tiebao Meng ◽  
Zhicheng Zhang ◽  
Xiaokun Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Fine Tuning ◽  
Learning Tools ◽  
Pros And Cons ◽  
Future Work

This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.

scholarly journals Superresolution Reconstruction of Video Based on Efficient Subpixel Convolutional Neural Network for Urban Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jie Shen ◽  
Mengxi Xu ◽  
Xinyu Du ◽  
Yunbo Xiong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
Video Surveillance ◽  
Reconstruction Method ◽  
Motion Information ◽  
Learning Networks ◽  
Urban Computing ◽  
Deep Convolution Network

Video surveillance is an important data source of urban computing and intelligence. The low resolution of many existing video surveillance devices affects the efficiency of urban computing and intelligence. Therefore, improving the resolution of video surveillance is one of the important tasks of urban computing and intelligence. In this paper, the resolution of video is improved by superresolution reconstruction based on a learning method. Different from the superresolution reconstruction of static images, the superresolution reconstruction of video is characterized by the application of motion information. However, there are few studies in this area so far. Aimed at fully exploring motion information to improve the superresolution of video, this paper proposes a superresolution reconstruction method based on an efficient subpixel convolutional neural network, where the optical flow is introduced in the deep learning network. Fusing the optical flow features between successive frames can compensate for information in frames and generate high-quality superresolution results. In addition, in order to improve the superresolution, a superpixel convolution layer is added after the deep convolution network. Finally, experimental evaluations demonstrate the satisfying performance of our method compared with previous methods and other deep learning networks; our method is more efficient.

scholarly journals A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 84 ◽  
Author(s):  
Atif Mehmood ◽  
Muazzam Maqsood ◽  
Muzaffar Bashir ◽  
Yang Shuyuan
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Early Stage ◽  
The State ◽  
Test Accuracy ◽  
Learning Approaches

Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

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