A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

The synergy of computational logistics and deep learning provides a new methodology and solution to the operational decisions of container terminal handling systems (CTHS) at the strategic, tactical, and executive levels. Above all, the container terminal logistics tactical operational complexity is discussed by computational logistics, and the liner handling volume (LHV) has important influences on a series of terminal scheduling decision problems. Subsequently, a feature-extraction-based lightweight convolutional and recurrent neural network adaptive computing model (FEB-LCR-ACM) is presented initially to predict the LHV by the fusion of multiple deep learning algorithms and mechanisms, especially for the specific feature extraction package of tsfresh. Consequently, the container-terminal-oriented logistics service scheduling decision support design paradigm is put forward tentatively by FEB-LCR-ACM. Finally, a typical large-scale container terminal of China is chosen to implement, execute, and evaluate the FEB-LCR-ACM based on the terminal running log around the indicator of LHV. In the case of severe vibration of LHV between 2 twenty-foot equivalent units (TEUs) and 4215 TEUs, while forecasting the LHV of 300 liners by the log of five years, the forecasting error within 100 TEUs almost accounts for 80%. When predicting the operation of 350 ships by the log of six years, the forecasting deviation within 100 TEUs reaches up to nearly 90%. The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.

Download Full-text

A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

Sensors ◽

10.3390/s20143876 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3876 ◽

Cited By ~ 3

Author(s):

Tiantian Zhu ◽

Zhengqiu Weng ◽

Guolang Chen ◽

Lei Fu

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Mobile Devices ◽

Real World ◽

Large Scale ◽

User Authentication ◽

Mobile User ◽

Learning System ◽

Support Vector ◽

Motion Sensors

With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.

Download Full-text

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

10.2196/preprints.22865 ◽

2020 ◽

Author(s):

Anusha Ampavathi ◽

Vijaya Saradhi T

Keyword(s):

Feature Extraction ◽

Big Data ◽

Deep Learning ◽

Weight Function ◽

Optimization Algorithm ◽

Large Scale ◽

Heuristic Algorithms ◽

Disease Prediction ◽

Health Care Decisions ◽

Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

Download Full-text

Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

BMC Nephrology ◽

10.1186/s12882-021-02421-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tianqi Tu ◽

Xueling Wei ◽

Yue Yang ◽

Nianrong Zhang ◽

Wei Li ◽

...

Keyword(s):

Deep Learning ◽

Renal Biopsy ◽

Membranous Nephropathy ◽

Learning Algorithm ◽

Hyperspectral Imagery ◽

Chinese Patients ◽

Support Vector ◽

Deep Learning Algorithm ◽

The Difference ◽

Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

Download Full-text

Smart paddy field monitoring system using deep learning and IoT

Concurrent Engineering ◽

10.1177/1063293x21988944 ◽

2021 ◽

pp. 1063293X2198894

Author(s):

Prabira Kumar Sethy ◽

Santi Kumari Behera ◽

Nithiyakanthan Kannan ◽

Sridevi Narayanan ◽

Chanki Pandey

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Transfer Learning ◽

Paddy Field ◽

New Technology ◽

Support Vector ◽

Nitrogen Status ◽

Deep Feature ◽

Per Capita ◽

Deep Feature Extraction

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

Download Full-text

Fast Recognition System for Tree Images based on Caffe Platform and Deep Learning

10.21203/rs.3.rs-52291/v1 ◽

2020 ◽

Author(s):

Zongchen Li ◽

Wenzhuo Zhang ◽

Guoxiong Zhou

Keyword(s):

Deep Learning ◽

Dual Task ◽

Learning Algorithm ◽

Recognition System ◽

Support Vector ◽

Similar Species ◽

Graphical Interface ◽

Fast Recognition ◽

System Solution ◽

Tree Image

Abstract Aiming at the difficult problem of complex extraction for tree image in the existing complex background, we took tree species as the research object and proposed a fast recognition system solution for tree image based on Caffe platform and deep learning. In the research of deep learning algorithm based on Caffe framework, the improved Dual-Task CNN model (DCNN) is applied to train the image extractor and classifier to accomplish the dual tasks of image cleaning and tree classification. In addition, when compared with the traditional classification methods represented by Support Vector Machine (SVM) and Single-Task CNN model, Dual-Task CNN model demonstrates its superiority in classification performance. Then, in order for further improvement to the recognition accuracy for similar species, Gabor kernel was introduced to extract the features of frequency domain for images in different scales and directions, so as to enhance the texture features of leaf images and improve the recognition effect. The improved model was tested on the data sets of similar species. As demonstrated by the results, the improved deep Gabor convolutional neural network (GCNN) is advantageous in tree recognition and similar tree classification when compared with the Dual-Task CNN classification method. Finally, the recognition results of trees can be displayed on the application graphical interface as well. In the application graphical interface designed based on Ubantu system, it is capable to perform such functions as quick reading of and search for picture files, snapshot, one-key recognition, one-key e

Download Full-text

Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

Revista de Teledetección ◽

10.4995/raet.2020.13337 ◽

2020 ◽

pp. 35

Author(s):

M. Campos-Taberner ◽

F.J. García-Haro ◽

B. Martínez ◽

M.A. Gilabert

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Near Infrared ◽

Spatial Information ◽

Learning Algorithm ◽

Support Vector ◽

Sensing Applications ◽

Learning Techniques ◽

Remote Sensing Applications ◽

Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

Download Full-text

Research on Efficient Deep Learning Algorithm Based on ShuffleGhost in the Field of Virtual Reality

Wireless Communications and Mobile Computing ◽

10.1155/2021/1382781 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Bangtong Huang ◽

Hongquan Zhang ◽

Zihong Chen ◽

Lingling Li ◽

Lihua Shi

Keyword(s):

Virtual Reality ◽

Deep Learning ◽

Large Scale ◽

Learning Algorithm ◽

Feature Maps ◽

Embedded Devices ◽

Feature Map ◽

Deep Learning Algorithm ◽

Proper Design ◽

The Cost

Deep learning algorithms are facing the limitation in virtual reality application due to the cost of memory, computation, and real-time computation problem. Models with rigorous performance might suffer from enormous parameters and large-scale structure, and it would be hard to replant them onto embedded devices. In this paper, with the inspiration of GhostNet, we proposed an efficient structure ShuffleGhost to make use of the redundancy in feature maps to alleviate the cost of computations, as well as tackling some drawbacks of GhostNet. Since GhostNet suffers from high computation of convolution in Ghost module and shortcut, the restriction of downsampling would make it more difficult to apply Ghost module and Ghost bottleneck to other backbone. This paper proposes three new kinds of ShuffleGhost structure to tackle the drawbacks of GhostNet. The ShuffleGhost module and ShuffleGhost bottlenecks are utilized by the shuffle layer and group convolution from ShuffleNet, and they are designed to redistribute the feature maps concatenated from Ghost Feature Map and Primary Feature Map. Besides, they eliminate the gap of them and extract the features. Then, SENet layer is adopted to reduce the computation cost of group convolution, as well as evaluating the importance of the feature maps which concatenated from Ghost Feature Maps and Primary Feature Maps and giving proper weights for the feature maps. This paper conducted some experiments and proved that the ShuffleGhostV3 has smaller trainable parameters and FLOPs with the ensurance of accuracy. And with proper design, it could be more efficient in both GPU and CPU side.

Download Full-text

Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Frontiers in Public Health ◽

10.3389/fpubh.2021.670352 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ashwini K ◽

P. M. Durai Raj Vincent ◽

Kathiravan Srinivasan ◽

Chuan-Yu Chang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Support Vector ◽

Svm Classifier ◽

Infant Cry ◽

Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

Download Full-text

Research on Feature Extracted Method for Flutter Test Based on EMD and CNN

International Journal of Aerospace Engineering ◽

10.1155/2021/6620368 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Hua Zheng ◽

Zhenglong Wu ◽

Shiqiang Duan ◽

Jiangtao Zhou

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Wind Tunnel ◽

Signal Analysis ◽

Learning Algorithm ◽

Structural Information ◽

Structural Response ◽

Training Dataset ◽

Measured Signal ◽

Test Dataset

Due to the inevitable deviations between the results of theoretical calculations and physical experiments, flutter tests and flutter signal analysis often play significant roles in designing the aeroelasticity of a new aircraft. The measured structural response from aeroelastic models in both wind tunnel tests and real fight flutter tests contain an abundance of structural information, but traditional methods tend to have limited ability to extract features of concern. Inspired by deep learning concepts, a novel feature extraction method for flutter signal analysis was established in this study by combining the convolutional neural network (CNN) with empirical mode decomposition (EMD). It is widely hypothesized that when flutter occurs, the measured structural signals are harmonic or divergent in the time domain, and that the flutter modal (1) is singular and (2) its energy increases significantly in the frequency domain. A measured-signal feature extraction and flutter criterion framework was constructed accordingly. The measured signals from a wind tunnel test were manually labeled “flutter” and “no-flutter” as the foundational dataset for the deep learning algorithm. After the normalized preprocessing, the intrinsic mode functions (IMFs) of the flutter test signals are obtained by the EMD method. The IMFs are then reshaped to make them the suitable size to be input to the CNN. The CNN parameters are optimized though the training dataset, and the trained model is validated through the test dataset (i.e., cross-validation). The accuracy rate of the proposed method reached 100% on the test dataset. The training model appears to effectively distinguish whether or not the structural response signal contains flutter. The combination of EMD and CNN provides effective feature extraction of time series signals in flutter test data. This research explores the connection between structural response signals and flutter from the perspective of artificial intelligence. The method allows for real-time, online prediction with low computational complexity.

Download Full-text