A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning

Automatically detecting mud in bauxite ores is important and valuable, with which we can improve productivity and reduce pollution. However, distinguishing mud and ores in a real scene is challenging for their similarity in shape, color, and texture. Moreover, training a deep learning model needs a large amount of exactly labeled samples, which is expensive and time consuming. Aiming at the challenging problem, this paper proposed a novel weakly supervised method based on deep active learning (AL), named YOLO-AL. The method uses the YOLO-v3 model as the basic detector, which is initialized with the pretrained weights on the MS COCO dataset. Then, an AL framework-embedded YOLO-v3 model is constructed. In the AL process, it iteratively fine-tunes the last few layers of the YOLO-v3 model with the most valuable samples, which is selected by a Less Confident (LC) strategy. Experimental results show that the proposed method can effectively detect mud in ores. More importantly, the proposed method can obviously reduce the labeled samples without decreasing the detection accuracy.

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Accelerating the Automated Detection, Counting and Measurements of Reproductive Organs in Herbarium Collections in the Era of Deep Learning

Biodiversity Information Science and Standards ◽

10.3897/biss.3.37341 ◽

2019 ◽

Vol 3 ◽

Author(s):

Adán Mora-Fallas ◽

Hervé Goëau ◽

Susan Mazer ◽

Natalie Love ◽

Erick Mata-Montero ◽

...

Keyword(s):

Deep Learning ◽

Active Learning ◽

Reproductive Organs ◽

Learning Model ◽

Learning Technologies ◽

Training Dataset ◽

Basic Information ◽

Learning Mechanism ◽

Deep Learning Model ◽

Instance Segmentation

Millions of herbarium records provide an invaluable legacy and knowledge of the spatial and temporal distributions of plants over centuries across all continents (Soltis et al. 2018). Due to recent efforts to digitize and to make publicly accessible most major natural collections, investigations of ecological and evolutionary patterns at unprecedented geographic scales are now possible (Carranza-Rojas et al. 2017, Lorieul et al. 2019). Nevertheless, biologists are now facing the problem of extracting from a huge number of herbarium sheets basic information such as textual descriptions, the numbers of organs, and measurements of various morphological traits. Deep learning technologies can dramatically accelerate the extraction of such basic information by automating the routines of organ identification, counts and measurements, thereby allowing biologists to spend more time on investigations such as phenological or geographic distribution studies. Recent progress on instance segmentation demonstrated by the Mask-RCNN method is very promising in the context of herbarium sheets, in particular for detecting with high precision different organs of interest on each specimen, including leaves, flowers, and fruits. However, like any deep learning approach, this method requires a significant number of labeled examples with fairly detailed outlines of individual organs. Creating such a training dataset can be very time-consuming and may be discouraging for researchers. We propose in this work to integrate the Mask-RCNN approach within a global system enabling an active learning mechanism (Sener and Savarese 2018) in order to minimize the number of outlines of organs that researchers must manually annotate. The principle is to alternate cycles of manual annotations and training updates of the deep learning model and predictions on the entire collection to process. Then, the challenge of the active learning mechanism is to estimate automatically at each cycle which are the most useful objects that must be manually extracted in the next manual annotation cycle in order to learn, in as few cycles as possible, an accurate model. We discuss experiments addressing the effectiveness, the limits and the time required of our approach for annotation, in the context of a phenological study of more than 10,000 reproductive organs (buds, flowers, fruits and immature fruits) of Streptanthus tortuosus, a species known to be highly variable in appearance and therefore very difficult to be processed by an instance segmentation deep learning model.

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Advancing PICO element detection in biomedical text via deep neural networks

Bioinformatics ◽

10.1093/bioinformatics/btaa256 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3856-3862

Author(s):

Di Jin ◽

Peter Szolovits

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Conditional Random Field ◽

Contextual Information ◽

Learning Model ◽

Detection Accuracy ◽

Clinical Question ◽

Specific Patient ◽

Benchmark Datasets ◽

Deep Learning Model

Abstract Motivation In evidence-based medicine, defining a clinical question in terms of the specific patient problem aids the physicians to efficiently identify appropriate resources and search for the best available evidence for medical treatment. In order to formulate a well-defined, focused clinical question, a framework called PICO is widely used, which identifies the sentences in a given medical text that belong to the four components typically reported in clinical trials: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). In this work, we propose a novel deep learning model for recognizing PICO elements in biomedical abstracts. Based on the previous state-of-the-art bidirectional long-short-term memory (bi-LSTM) plus conditional random field architecture, we add another layer of bi-LSTM upon the sentence representation vectors so that the contextual information from surrounding sentences can be gathered to help infer the interpretation of the current one. In addition, we propose two methods to further generalize and improve the model: adversarial training and unsupervised pre-training over large corpora. Results We tested our proposed approach over two benchmark datasets. One is the PubMed-PICO dataset, where our best results outperform the previous best by 5.5%, 7.9% and 5.8% for P, I and O elements in terms of F1 score, respectively. And for the other dataset named NICTA-PIBOSO, the improvements for P/I/O elements are 3.9%, 15.6% and 1.3% in F1 score, respectively. Overall, our proposed deep learning model can obtain unprecedented PICO element detection accuracy while avoiding the need for any manual feature selection. Availability and implementation Code is available at https://github.com/jind11/Deep-PICO-Detection.

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Android Malware Detection Based on a Hybrid Deep Learning Model

Security and Communication Networks ◽

10.1155/2020/8863617 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Tianliang Lu ◽

Yanhui Du ◽

Li Ouyang ◽

Qiuyu Chen ◽

Xirui Wang

Keyword(s):

Deep Learning ◽

Learning Algorithms ◽

Malware Detection ◽

Learning Model ◽

Machine Learning Algorithms ◽

Detection Accuracy ◽

Dynamic Feature ◽

Android Malware ◽

Android Malware Detection ◽

Deep Learning Model

In recent years, the number of malware on the Android platform has been increasing, and with the widespread use of code obfuscation technology, the accuracy of antivirus software and traditional detection algorithms is low. Current state-of-the-art research shows that researchers started applying deep learning methods for malware detection. We proposed an Android malware detection algorithm based on a hybrid deep learning model which combines deep belief network (DBN) and gate recurrent unit (GRU). First of all, analyze the Android malware; in addition to extracting static features, dynamic behavioral features with strong antiobfuscation ability are also extracted. Then, build a hybrid deep learning model for Android malware detection. Because the static features are relatively independent, the DBN is used to process the static features. Because the dynamic features have temporal correlation, the GRU is used to process the dynamic feature sequence. Finally, the training results of DBN and GRU are input into the BP neural network, and the final classification results are output. Experimental results show that, compared with the traditional machine learning algorithms, the Android malware detection model based on hybrid deep learning algorithms has a higher detection accuracy, and it also has a better detection effect on obfuscated malware.

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Auxiliary Equipment Detection in Marine Engine Rooms Based on Deep Learning Model

Journal of Marine Science and Engineering ◽

10.3390/jmse9091006 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1006

Author(s):

Jiahao Qi ◽

Jundong Zhang ◽

Qingyan Meng

Keyword(s):

Deep Learning ◽

Single Channel ◽

Model Performance ◽

Learning Model ◽

Detection Accuracy ◽

Auxiliary Equipment ◽

Marine Engine ◽

Engine Room ◽

Pipeline Networks ◽

Deep Learning Model

In the intelligent perception of the marine engine room, visual identification of auxiliary equipment is the prerequisite for defect recognition and anomaly detection. To improve the detection accuracy, this study presents an auxiliary equipment detector in the cabin based on a deep learning model. Owing to the compact layout of pipeline networks and the large disparity in the equipment scales, we initially adopted RetinaNet as the basic framework, and introduced the single channel plain architecture RepVGG as the feature extraction network to simplify the complexity and improve realtime detection. Secondly, the Neighbor Erasing and Transferring Mechanism (NETM) was applied in the feature pyramid to deal with more complicated scale variations. Then, the complete IoU (CIoU) regression loss function was used instead of smooth L1, and the DIoU Soft-NMS mechanism was proposed to alleviate the misdetection in congested cabins. Further, comparison experiments and ablation experiments were performed on the auxiliary equipment in a marine engine room (AEMER) dataset to validate the efficacy of these strategies on the model performance boost. Specifically, our model can correctly detect 93.44% of coolers, 100.00% of diesel engines, 60.26% of meters, 95.30% of pumps, 55.01% of reservoirs, 97.68% of oil separators, and 74.37% of valves in a practical cabin.

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A Real-Time Vehicle Detection System under Various Bad Weather Conditions Based on a Deep Learning Model without Retraining

Sensors ◽

10.3390/s20205731 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5731 ◽

Cited By ~ 1

Author(s):

Xiu-Zhi Chen ◽

Chieh-Min Chang ◽

Chao-Wei Yu ◽

Yen-Lin Chen

Keyword(s):

Deep Learning ◽

Detection System ◽

Vehicle Detection ◽

Weather Conditions ◽

Learning Model ◽

Detection Methods ◽

Computational Time ◽

Detection Accuracy ◽

Bad Weather ◽

Deep Learning Model

Numerous vehicle detection methods have been proposed to obtain trustworthy traffic data for the development of intelligent traffic systems. Most of these methods perform sufficiently well under common scenarios, such as sunny or cloudy days; however, the detection accuracy drastically decreases under various bad weather conditions, such as rainy days or days with glare, which normally happens during sunset. This study proposes a vehicle detection system with a visibility complementation module that improves detection accuracy under various bad weather conditions. Furthermore, the proposed system can be implemented without retraining the deep learning models for object detection under different weather conditions. The complementation of the visibility was obtained through the use of a dark channel prior and a convolutional encoder–decoder deep learning network with dual residual blocks to resolve different effects from different bad weather conditions. We validated our system on multiple surveillance videos by detecting vehicles with the You Only Look Once (YOLOv3) deep learning model and demonstrated that the computational time of our system could reach 30 fps on average; moreover, the accuracy increased not only by nearly 5% under low-contrast scene conditions but also 50% under rainy scene conditions. The results of our demonstrations indicate that our approach is able to detect vehicles under various bad weather conditions without the need to retrain a new model.

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Fake Detect: A Deep Learning Ensemble Model for Fake News Detection

Complexity ◽

10.1155/2021/5557784 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Nida Aslam ◽

Irfan Ullah Khan ◽

Farah Salem Alotaibi ◽

Lama Abdulaziz Aldaej ◽

Asma Khaled Aldubaikil

Keyword(s):

Social Media ◽

Deep Learning ◽

Learning Model ◽

Experimental Results ◽

Ensemble Model ◽

Fake News ◽

Learning Models ◽

Social Media Networks ◽

Deep Learning Model

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset.

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A BERT model generates diagnostically relevant semantic embeddings from pathology synopses with active learning

Communications Medicine ◽

10.1038/s43856-021-00008-0 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Youqing Mu ◽

Hamid R. Tizhoosh ◽

Rohollah Moosavi Tayebi ◽

Catherine Ross ◽

Monalisa Sur ◽

...

Keyword(s):

Deep Learning ◽

Active Learning ◽

Visual Information ◽

Language Model ◽

Clinical Information ◽

Relevant Information ◽

Learning Model ◽

Training Data ◽

Domain Specific Knowledge ◽

Deep Learning Model

Abstract Background Pathology synopses consist of semi-structured or unstructured text summarizing visual information by observing human tissue. Experts write and interpret these synopses with high domain-specific knowledge to extract tissue semantics and formulate a diagnosis in the context of ancillary testing and clinical information. The limited number of specialists available to interpret pathology synopses restricts the utility of the inherent information. Deep learning offers a tool for information extraction and automatic feature generation from complex datasets. Methods Using an active learning approach, we developed a set of semantic labels for bone marrow aspirate pathology synopses. We then trained a transformer-based deep-learning model to map these synopses to one or more semantic labels, and extracted learned embeddings (i.e., meaningful attributes) from the model’s hidden layer. Results Here we demonstrate that with a small amount of training data, a transformer-based natural language model can extract embeddings from pathology synopses that capture diagnostically relevant information. On average, these embeddings can be used to generate semantic labels mapping patients to probable diagnostic groups with a micro-average F1 score of 0.779 Â ± 0.025. Conclusions We provide a generalizable deep learning model and approach to unlock the semantic information inherent in pathology synopses toward improved diagnostics, biodiscovery and AI-assisted computational pathology.

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On The Effect Of Decomposition Granularity On DeTraC For COVID-19 Detection Using Chest X-Ray Images

ECMS 2021 Proceedings edited by Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela ◽

10.7148/2021-0029 ◽

2021 ◽

Author(s):

Nicole P. Mugova ◽

Mohammed M. Abdelsamea ◽

Mohamed M. Gaber

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Learning Model ◽

Deep Convolutional Neural Network ◽

Experimental Results ◽

Number Of Clusters ◽

X Ray ◽

Chest X Ray ◽

Deep Learning Model

Covid-19 is a growing issue in society and there is a need for resources to manage the disease. This paper looks at studying the effect of class decomposition in our previously proposed deep Convolutional Neural Network, called DeTraC (Decompose, Transfer and Compose). DeTraC has the ability to robustly detect and predict Covid-19 from chest X-ray images. The experimental results showed that changing the number of clusters affected the performance of DeTraC and influenced the accuracy of the model. As the number of clusters increased, the accuracy decreased for the shallow tuning mode but increased for the deep tuning mode. This shows the importance of using suitable hyperparameter settings in order to get the best results from a deep learning model. The highest accuracy obtained, in this study, was 98.33% from the deep tuning model.

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A hybrid deep-learning model for fault diagnosis of rolling bearings under strong noise environment

Measurement Science and Technology ◽

10.1088/1361-6501/ac4a18 ◽

2022 ◽

Author(s):

Ke Zhang ◽

Caizi Fan ◽

Xiaochen Zhang ◽

Huaitao Shi ◽

Songhua Li

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Recognition Rate ◽

Vibration Signal ◽

Rolling Bearing ◽

Learning Model ◽

Detection Accuracy ◽

Practical Applications ◽

Reconstructed Signal ◽

Deep Learning Model

Abstract Aiming at the problem that the signal of rolling bearing is interfered by strong noise in practical engineering environment, which leads to the decline of the diagnosis accuracy of intelligent diagnosis model. This paper proposes a novel hybrid model (CDAE-BLCNN). First, the rolling bearing vibration signal containing noise was input into the Convolutional Denoising Auto-Encoder (CDAE), which denoises the signal through unsupervised learning, and then outputs the reconstructed data. Secondly, a hybrid neural network (BLCNN) composed of multi-scale wide convolution kernel block (MWCNN) and bidirectional long-short-term memory network (BiLSTM) was used to extract intrinsic fault features from the reconstructed signal and diagnose fault types. The analysis results demonstrate that the proposed hybrid deep learning model achieves higher detection accuracy even under different noise and various rotating speed. Compared with other models, there is a high fault recognition rate, robustness, and generalization ability, which may be favorable to practical applications.

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Off-Topic Detection of Business English Essay Based on Deep Learning Model

Mobile Information Systems ◽

10.1155/2021/5051667 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yiting Zhu

Keyword(s):

Deep Learning ◽

Detection Method ◽

Learning Model ◽

Detection Methods ◽

Detection Accuracy ◽

Topic Detection ◽

Business English ◽

Automatic Scoring ◽

Deep Learning Model ◽

Detection Effect

The automatic scoring system of business English essay has been widely used in the field of education, and it is indispensable for the task of off-topic detection of essay. Most of the traditional off-topic detection methods convert text into vector representation of vector space and then calculate the similarity between the text and the correct text to get the off-topic result. However, those methods only focus on the structure of the text, but ignore the semantic association. In addition, the traditional detection method has a low off-topic detection effect for essays with high divergence. In view of the above problems, this paper proposes an off-topic detection method for business English essay based on the deep learning model. Firstly, the word2vec model is used to represent words in sentences as word vectors. And, LDA is used to extract the vector of topic and text, respectively. Then, word vector and topic word vector are spliced together as the input of the convolutional neural network (CNN). CNN is used to extract and screen the features of sentences and perform similarity calculation. When the similarity is less than the threshold, the paper also maps the topic and the subject words in the coupling space and calculates their relevance. Finally, unsupervised off-topic detection is realized by the clustering method. The experimental results show that the off-topic detection method based on the deep learning model can improve the detection accuracy of both the essays with low divergence and the essays with high divergence to a certain extent, especially the essays with high divergence.

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