scholarly journals Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 193 ◽  
Author(s):  
Zihao Huang ◽  
Gang Huang ◽  
Zhijun Chen ◽  
Chaozhong Wu ◽  
Xiaofeng Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Travel Demand ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Image Feature ◽  
Support Vector ◽  
Data Set ◽  
Demand Distribution ◽  
Demand Forecasting Model

With the development of online cars, the demand for travel prediction is increasing in order to reduce the information asymmetry between passengers and drivers of online car-hailing. This paper proposes a travel demand forecasting model named OC-CNN based on the convolutional neural network to forecast the travel demand. In order to make full use of the spatial characteristics of the travel demand distribution, this paper meshes the prediction area and creates a travel demand data set of the graphical structure to preserve its spatial properties. Taking advantage of the convolutional neural network in image feature extraction, the historical demand data of the first twenty-five minutes of the entire region are used as a model input to predict the travel demand for the next five minutes. In order to verify the performance of the proposed method, one-month data from online car-hailing of the Chengdu Fourth Ring Road are used. The results show that the model successfully extracts the spatiotemporal features of the data, and the prediction accuracies of the proposed method are superior to those of the representative methods, including the Bayesian Ridge Model, Linear Regression, Support Vector Regression, and Long Short-Term Memory networks.

scholarly journals HARTH: A Human Activity Recognition Dataset for Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7853
Author(s):  
Aleksej Logacjov ◽  
Kerstin Bach ◽  
Atle Kongsvold ◽  
Hilde Bremseth Bårdstu ◽  
Paul Jarle Mork
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Free Living

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

Patient visit forecasting in an emergency department using a deep neural network approach

Kybernetes ◽  
2019 ◽  
Vol 49 (9) ◽  
pp. 2335-2348 ◽  
Author(s):  
Milad Yousefi ◽  
Moslem Yousefi ◽  
Masood Fathi ◽  
Flavio S. Fogliatto
Keyword(s):  
Neural Network ◽  
Emergency Department ◽  
Linear Regression ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Neural Network Approach ◽  
Content Type

Purpose This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days. Design/methodology/approach In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor. Findings Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression. Research limitations/implications The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications. Originality/value To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

A Dynamic Convolutional Neural Network Based Shared-Bike Demand Forecasting Model

2021 ◽  
Vol 12 (6) ◽  
pp. 1-24
Author(s):  
Shaojie Qiao ◽  
Nan Han ◽  
Jianbin Huang ◽  
Kun Yue ◽  
Rui Mao ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Learning Models ◽  
Bike Sharing ◽  
Demand Forecasting Model ◽  
Machine Learning Models

Bike-sharing systems are becoming popular and generate a large volume of trajectory data. In a bike-sharing system, users can borrow and return bikes at different stations. In particular, a bike-sharing system will be affected by weather, the time period, and other dynamic factors, which challenges the scheduling of shared bikes. In this article, a new shared-bike demand forecasting model based on dynamic convolutional neural networks, called SDF , is proposed to predict the demand of shared bikes. SDF chooses the most relevant weather features from real weather data by using the Pearson correlation coefficient and transforms them into a two-dimensional dynamic feature matrix, taking into account the states of stations from historical data. The feature information in the matrix is extracted, learned, and trained with a newly proposed dynamic convolutional neural network to predict the demand of shared bikes in a dynamical and intelligent fashion. The phase of parameter update is optimized from three aspects: the loss function, optimization algorithm, and learning rate. Then, an accurate shared-bike demand forecasting model is designed based on the basic idea of minimizing the loss value. By comparing with classical machine learning models, the weight sharing strategy employed by SDF reduces the complexity of the network. It allows a high prediction accuracy to be achieved within a relatively short period of time. Extensive experiments are conducted on real-world bike-sharing datasets to evaluate SDF. The results show that SDF significantly outperforms classical machine learning models in prediction accuracy and efficiency.

scholarly journals Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network

2019 ◽  
Vol 10 (2) ◽  
pp. 98-110 ◽  
Author(s):  
Keke Zhang ◽  
Lei Zhang ◽  
Qiufeng Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Back Propagation ◽  
Support Vector ◽  
Automatic Identification ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Leaf Disease

The cherry leaves infected by Podosphaera pannosa will suffer powdery mildew, which is a serious disease threatening the cherry production industry. In order to identify the diseased cherry leaves in early stage, the authors formulate the cherry leaf disease infected identification as a classification problem and propose a fully automatic identification method based on convolutional neural network (CNN). The GoogLeNet is used as backbone of the CNN. Then, transferred learning techniques are applied to fine-tune the CNN from pre-trained GoogLeNet on ImageNet dataset. This article compares the proposed method against three traditional machine learning methods i.e., support vector machine (SVM), k-nearest neighbor (KNN) and back propagation (BP) neural network. Quantitative evaluations conducted on a data set of 1,200 images collected by smart phones, demonstrates that the CNN achieves best precise performance in identifying diseased cherry leaves, with the testing accuracy of 99.6%. Thus, a CNN can be used effectively in identifying the diseased cherry leaves.

scholarly journals Three-Class Mammogram Classification Based on Descriptive CNN Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
M. Mohsin Jadoon ◽  
Qianni Zhang ◽  
Ihsan Ul Haq ◽  
Sharjeel Butt ◽  
Adeel Jadoon
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Curvelet Transform ◽  
Data Matrix ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Accuracy Rate ◽  
Data Set ◽  
Proposed Model ◽  
Mammogram Images

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

scholarly journals INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA

2017 ◽  
Vol IV-1/W1 ◽  
pp. 115-123 ◽  
Author(s):  
R. Niessner ◽  
H. Schilling ◽  
B. Jutzi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Vehicle Classification ◽  
Support Vector ◽  
Lidar Data ◽  
Data Set

In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks. To study the potential of the Convolutional Neural Network, in this paper three approaches are investigated to train classifiers based on Convolutional Neural Networks. These approaches allow Convolutional Neural Networks to be trained on datasets containing only a few hundred training samples, which results in a successful classification. Two of these approaches are based on the concept of transfer learning. In the first approach features, created by a pretrained Convolutional Neural Network, are used for a classification using a support vector machine. In the second approach a pretrained Convolutional Neural Network gets fine-tuned on a different data set. The third approach includes the design and training for flat Convolutional Neural Networks from the scratch. The evaluation of the proposed approaches is based on a data set provided by the IEEE Geoscience and Remote Sensing Society (GRSS) which contains RGB and LiDAR data of an urban area. In this work it is shown that these Convolutional Neural Networks lead to classification results with high accuracy both on RGB and LiDAR data. Features which are derived by RGB data transferred into LiDAR data by transfer learning lead to better results in classification in contrast to RGB data. Using a neural network which contains fewer layers than common neural networks leads to the best classification results. In this framework, it can furthermore be shown that the practical application of LiDAR images results in a better data basis for classification of vehicles than the use of RGB images.

scholarly journals Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259283
Author(s):  
Wentong Wu ◽  
Han Liu ◽  
Lingling Li ◽  
Yilin Long ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Detection Method ◽  
Image Feature ◽  
Remote Sensing Images ◽  
Data Set ◽  
Detection Ability ◽  
Small Targets

This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.

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