Development of a generative adversarial neural network for identification of potential HIV-1 inhibitors by deep learning methods

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block the region of the viral envelope protein gp120 critical for the virus binding to cellular receptor CD4 was developed using deep learning methods. The research were carried out to create the architecture of the neural network, to form virtual compound library of potential anti-HIV-1 agents for training the neural network, to make molecular docking of all compounds from this library with gp120, to calculate the values of binding free energy, to generate molecular fingerprints for chemical compounds from the training dataset. The training the neural network was implemented followed by estimation of the learning outcomes and work of the autoencoder. The validation of the neural network on a wide range of compounds from the ZINC database was carried out. The use of the neural network in combination with virtual screening of chemical databases was shown to form a productive platform for identifying the basic structures promising for the design of novel antiviral drugs that inhibit the early stages of HIV infection.

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Deep learning for multi-dimensional seismic impedance inversion

Geophysics ◽

10.1190/geo2020-0564.1 ◽

2021 ◽

pp. 1-44

Author(s):

Xinming Wu ◽

Shangsheng Yan ◽

Zhengfa Bi ◽

Sibo Zhang ◽

Hongjie Si

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Architecture ◽

Seismic Profile ◽

Thin Layers ◽

Well Logs ◽

Training Dataset ◽

Learning Methods ◽

Weak Supervision ◽

Impedance Model

Deep learning methods have shown promising performances in predicting acoustic impedance from seismic data which is typically considered as an ill-posed problem for traditional inver- sion schemes. Most of the deep learning methods, however, are based on a 1D neural network which is straightforward to implement but often yields unreasonable lateral discontinuities while predicting a multi-dimensional impedance model trace-by-trace. We introduce an improvement over the 1D network by replacing it with a 2D convolutional neural network (CNN) and incorporating the constraints of an initial impedance model. The initial model is fed to the network to provide a low-frequency trend control, which is helpful for both the 1D and 2D CNNs to yield stable impedance predictions. The proposed 2D CNN architecture is quite simple; however, due to lack of 2D impedance labels, training it is not straightforward. To prepare a 2D training dataset, we first define a random path that passes through multiple well logs. We then follow the path to extract a 2D seismic profile and an initial impedance profile which together form an input to the 2D CNN. The set of well logs (traversed by the path) serves as a partially labeled target. We train the 2D CNN with weak supervision by using an adaptive loss where the output 2D impedance model is adaptively evaluated at the well logs only in the partially labeled target. As the training datasets are randomly extracted in all directions in a 3D survey, the trained 2D CNN can predict a consistent 3D impedance model section-by-section in either inline or crossline directions. Synthetic and field examples show that the proposed 2D CNN is more robust to noise, recovers thin layers better, and yields a laterally more consistent impedance model than a 1D CNN with the same network architecture and the same training logs.

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De-aliased seismic data interpolation using a deep learning-based prediction-error filter

Geophysics ◽

10.1190/geo2020-0487.1 ◽

2021 ◽

pp. 1-63

Author(s):

Wenqian Fang ◽

Lihua Fu ◽

Shaoyong Liu ◽

Hongwei Li

Keyword(s):

Neural Network ◽

Deep Learning ◽

Seismic Data ◽

Prediction Error ◽

Training Data ◽

Training Dataset ◽

Data Interpolation ◽

Filter Methods ◽

The Neural Network ◽

Trained Network

Deep learning (DL) technology has emerged as a new approach for seismic data interpolation. DL-based methods can automatically learn the mapping between regularly subsampled and complete data from a large training dataset. Subsequently, the trained network can be used to directly interpolate new data. Therefore, compared with traditional methods, DL-based methods reduce the manual workload and render the interpolation process efficient and automatic by avoiding the selection of hyperparameters. However, two limitations of DL-based approaches exist. First, the generalization performance of the neural network is inadequate when processing new data with a different structure compared to the training data. Second, the interpretation of the trained networks is very difficult. To overcome these limitations, we combine the deep neural network and classic prediction-error filter methods, proposing a novel seismic data de-aliased interpolation framework termed PEFNet (Prediction-Error Filters Network). The PEFNet designs convolutional neural networks to learn the relationship between the subsampled data and the prediction-error filters. Thus, the filters estimated by the trained network are used for the recovery of missing traces. The learning of filters enables the network to better extract the local dip of seismic data and has a good generalization ability. In addition, PEFNet has the same interpretability as traditional prediction error-filter based methods. The applicability and the effectiveness of the proposed method are demonstrated here by synthetic and field data examples.

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Augmented Reality Maintenance Assistant Using YOLOv5

Applied Sciences ◽

10.3390/app11114758 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4758

Author(s):

Ana Malta ◽

Mateus Mendes ◽

Torres Farinha

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Recognition ◽

Augmented Reality ◽

Real Time ◽

Recognition System ◽

High Accuracy ◽

Video Streams ◽

The Neural Network ◽

Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

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Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

Scientific Reports ◽

10.1038/s41598-020-78129-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Young-Gon Kim ◽

Sungchul Kim ◽

Cristina Eunbee Cho ◽

In Hye Song ◽

Hee Jin Lee ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Frozen Section ◽

Medical Center ◽

External Validation ◽

Model Performance ◽

Classification Model ◽

Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

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Orchard Mapping with Deep Learning Semantic Segmentation

Sensors ◽

10.3390/s21113813 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3813

Author(s):

Athanasios Anagnostis ◽

Aristotelis C. Tagarakis ◽

Dimitrios Kateris ◽

Vasileios Moysiadis ◽

Claus Grøn Sørensen ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Semantic Segmentation ◽

Automated Detection ◽

Aerial Images ◽

Training Dataset ◽

Field Boundary ◽

Different Seasons ◽

Detection And Localization ◽

Different Levels

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

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A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽

10.3390/electronics10010081 ◽

2021 ◽

Vol 10 (1) ◽

pp. 81

Author(s):

Jianbin Xiong ◽

Dezheng Yu ◽

Shuangyin Liu ◽

Lei Shu ◽

Xiaochan Wang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Plant Species ◽

Image Recognition ◽

Recurrent Neural Network ◽

Plant Diseases ◽

Learning Methods ◽

Smart Agriculture ◽

Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

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Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽

10.3390/e23060667 ◽

2021 ◽

Vol 23 (6) ◽

pp. 667

Author(s):

Wei Chen ◽

Qiang Sun ◽

Xiaomin Chen ◽

Gangcai Xie ◽

Huiqun Wu ◽

...

Keyword(s):

Neural Network ◽

Systematic Review ◽

Deep Learning ◽

Heart Sounds ◽

Learning Approaches ◽

Automated Classification ◽

Learning Methods ◽

Artificial Intelligence Technology ◽

Medical Big Data ◽

Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

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Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Sensors ◽

10.3390/s19010210 ◽

2019 ◽

Vol 19 (1) ◽

pp. 210 ◽

Cited By ~ 32

Author(s):

Zied Tayeb ◽

Juri Fedjaev ◽

Nejla Ghaboosi ◽

Christoph Richter ◽

Lukas Everding ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Classification Performance ◽

Feature Engineering ◽

Learning Models ◽

Eeg Signals ◽

Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

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Automatic Handgun Detection with Deep Learning in Video Surveillance Images

Applied Sciences ◽

10.3390/app11136085 ◽

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.

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Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211072401 ◽

2022 ◽

pp. 095440702110724

Author(s):

Chenyu Zhou ◽

Liangyao Yu ◽

Yong Li ◽

Jian Song

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Stability Control ◽

Estimation Accuracy ◽

Accurate Estimation ◽

Commercial Vehicles ◽

Sideslip Angle ◽

The Neural Network ◽

Wide Range ◽

The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

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