A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell

Due to advancement of technology there is a large usage of social media which leads to demand for data in the web. This data is very helpful to categorize the opinions into different sentiments and general evaluating the mood of public. The current research contributions are towards to detect the complete separation of sentence regardless of their aspects. The computational observation of sentiments and opinions stated by people in written language. Examination of defies presented by informal and crisp micro blogging created the origins. The proposed work targets building up a model for conclusion characterization that investigates the product features. It is also addresses domain explicit vocabularies to offer an domain arranged methodology and subsequently dissect and extricate the purchaser opinion towards well known advanced cell marks in the course of recent years. This model describes the use of deep learning model such as recurrent neural network to get better accuracy over traditional machine learning methods such as Random forest, Naïve bayes. The RNN model got training accuracy 97.6% and testing accuracy 95.6% which are much better compared to traditional machine learning models.

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Intelligent Data Analytics for Diagnosing Melanoma Skin Lesions via Deep Learning in IoT System

Mobile Information Systems ◽

10.1155/2021/8700506 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Shixiang Zhang ◽

Shuaiqi Huang ◽

Hongkai Wu ◽

Zicong Yang ◽

Yinda Chen

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Histopathological Examination ◽

Diagnostic System ◽

Skin Lesions ◽

Learning Model ◽

Supervised Machine Learning ◽

Learning Methods ◽

Machine Learning Methods ◽

Deep Learning Model

Melanoma is considered to be one of the most dangerous human malignancy, which is diagnosed visually or by dermoscopic analysis and histopathological examination. However, as these traditional methods are based on human experience and implemented manually, there have been great limitations for general usability in current clinical practice. In this paper, a novel hybrid machine learning approach is proposed to identify melanoma for skin healthcare in various cases. The proposed approach consists of classic machine learning methods, including convolutional neural networks (CNNs), EfficientNet, and XGBoost supervised machine learning. In the proposed approach, a deep learning model is trained directly from raw pixels and image labels for classification of skin lesions. Then, solely based on modeling of various features from patients, an XGBoost model is adopted to predict skin cancer. Following that, a diagnostic system which composed of the deep learning model and XGBoost model is developed to further improve the prediction efficiency and accuracy. Different from experience-based methods and solely image-based machine learning methods, the proposed approach is developed based on the theory of deep learning and feature engineering. Experiments show that the hybrid model outperforms single model like the traditional deep learning model or XGBoost model. Moreover, the data-driven-based characteristics can help the proposed approach develop a guideline for image analysis in other medical applications.

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Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method

Scientific Reports ◽

10.1038/s41598-019-52552-4 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 5

Author(s):

Kai-Yao Huang ◽

Justin Bo-Kai Hsu ◽

Tzong-Yi Lee

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Amino Acid ◽

Learning Model ◽

Training Dataset ◽

Learning Methods ◽

Machine Learning Methods ◽

Lysine Succinylation ◽

Testing Dataset ◽

Deep Learning Model

Abstract Succinylation is a type of protein post-translational modification (PTM), which can play important roles in a variety of cellular processes. Due to an increasing number of site-specific succinylated peptides obtained from high-throughput mass spectrometry (MS), various tools have been developed for computationally identifying succinylated sites on proteins. However, most of these tools predict succinylation sites based on traditional machine learning methods. Hence, this work aimed to carry out the succinylation site prediction based on a deep learning model. The abundance of MS-verified succinylated peptides enabled the investigation of substrate site specificity of succinylation sites through sequence-based attributes, such as position-specific amino acid composition, the composition of k-spaced amino acid pairs (CKSAAP), and position-specific scoring matrix (PSSM). Additionally, the maximal dependence decomposition (MDD) was adopted to detect the substrate signatures of lysine succinylation sites by dividing all succinylated sequences into several groups with conserved substrate motifs. According to the results of ten-fold cross-validation, the deep learning model trained using PSSM and informative CKSAAP attributes can reach the best predictive performance and also perform better than traditional machine-learning methods. Moreover, an independent testing dataset that truly did not exist in the training dataset was used to compare the proposed method with six existing prediction tools. The testing dataset comprised of 218 positive and 2621 negative instances, and the proposed model could yield a promising performance with 84.40% sensitivity, 86.99% specificity, 86.79% accuracy, and an MCC value of 0.489. Finally, the proposed method has been implemented as a web-based prediction tool (CNN-SuccSite), which is now freely accessible at http://csb.cse.yzu.edu.tw/CNN-SuccSite/.

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Machine learning for early detection of sepsis: an internal and temporal validation study

JAMIA Open ◽

10.1093/jamiaopen/ooaa006 ◽

2020 ◽

Vol 3 (2) ◽

pp. 252-260 ◽

Cited By ~ 1

Author(s):

Armando D Bedoya ◽

Joseph Futoma ◽

Meredith E Clement ◽

Kristin Corey ◽

Nathan Brajer ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Organ Failure ◽

Learning Model ◽

Learning Methods ◽

Machine Learning Methods ◽

Clinical Scores ◽

C Statistic ◽

Temporal Validation ◽

Deep Learning Model

Abstract Objective Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice. Materials and Methods We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP–RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed. Results The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP–RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP–RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP–RNN outperform all 7 clinical risk score and machine learning comparisons. Conclusions We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.

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A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction

Electronics ◽

10.3390/electronics10010039 ◽

2020 ◽

Vol 10 (1) ◽

pp. 39

Author(s):

Zhiyuan Xie ◽

Shichang Du ◽

Jun Lv ◽

Yafei Deng ◽

Shiyao Jia

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Model ◽

Recurrent Network ◽

Remaining Useful Life ◽

Support Vector ◽

Second Phase ◽

Learning Methods ◽

Useful Life ◽

Deep Learning Model

Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment.

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Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat

Symmetry ◽

10.3390/sym13112012 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2012

Author(s):

Jiameng Gao ◽

Chengzhong Liu ◽

Junying Han ◽

Qinglin Lu ◽

Hengxing Wang ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Wheat Variety ◽

Learning Model ◽

Wheat Cultivars ◽

Learning Methods ◽

Wheat Varieties ◽

Deep Learning Model

Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production.

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Using Machine Learning to Generate Novel Hypotheses: Increasing Optimism About COVID-19 Makes People Less Willing to Justify Unethical Behaviors

Psychological Science ◽

10.1177/0956797620959594 ◽

2020 ◽

Vol 31 (10) ◽

pp. 1222-1235

Author(s):

Abhishek Sheetal ◽

Zhiyu Feng ◽

Krishna Savani

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Unethical Behavior ◽

Past Research ◽

World Values Survey ◽

Learning Methods ◽

Social Distancing ◽

Machine Learning Methods ◽

Unethical Behaviors ◽

Deep Learning Model

How can we nudge people to not engage in unethical behaviors, such as hoarding and violating social-distancing guidelines, during the COVID-19 pandemic? Because past research on antecedents of unethical behavior has not provided a clear answer, we turned to machine learning to generate novel hypotheses. We trained a deep-learning model to predict whether or not World Values Survey respondents perceived unethical behaviors as justifiable, on the basis of their responses to 708 other items. The model identified optimism about the future of humanity as one of the top predictors of unethicality. A preregistered correlational study ( N = 218 U.S. residents) conceptually replicated this finding. A preregistered experiment ( N = 294 U.S. residents) provided causal support: Participants who read a scenario conveying optimism about the COVID-19 pandemic were less willing to justify hoarding and violating social-distancing guidelines than participants who read a scenario conveying pessimism. The findings suggest that optimism can help reduce unethicality, and they document the utility of machine-learning methods for generating novel hypotheses.

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Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics

Remote Sensing ◽

10.3390/rs13071360 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1360

Author(s):

A-Xing Zhu ◽

Fang-He Zhao ◽

Hao-Bo Pan ◽

Jun-Zhi Liu

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Rice Paddy ◽

Learning Model ◽

Field Sampling ◽

Learning Methods ◽

Training Samples ◽

Field Samples ◽

Deep Learning Model

Two main approaches are used in mapping rice paddy distribution from remote sensing images: phenological methods or machine learning methods. The phenological methods can map rice paddy distribution in a simple way but with limited accuracy. Machine learning, particularly deep learning, methods that learn the spectral signatures can achieve higher accuracy yet require a large number of field samples. This paper proposed a pheno-deep method to couple the simplicity of the phenological methods and the learning ability of the deep learning methods for mapping rice paddy at high accuracy without the need of field samples. The phenological method was first used to initially delineate the rice paddy for the purpose of creating training samples. These samples were then used to train the deep learning model. The trained deep learning model was applied to map the spatial distribution of rice paddy. The effectiveness of the pheno-deep method was evaluated in Jin’an District, Lu’an City, Anhui Province, China. Results show that the pheno-deep method achieved a high performance with the overall accuracy, the precision, the recall, and AUC (area under curve) being 88.8%, 87.2%, 91.1%, and 94.4%, respectively. The pheno-deep method achieved a much better performance than the phenological alone method and can overcome the noises in the training samples from the phenological method. The overall accuracy of the pheno-deep method is only 2.4% lower than that of the deep learning alone method trained with field samples and this difference is not statistically significant. In addition, the pheno-deep method requires no field sampling, which would be a noteworthy advantage for situations when large training samples are difficult to obtain. This study shows that by combining knowledge-based methods with data-driven methods, it is possible to achieve high mapping accuracy of geographic variables using remote sensing even with little field sampling efforts.

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Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽

10.3390/en14154595 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4595

Author(s):

Parisa Asadi ◽

Lauren E. Beckingham

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Ct Images ◽

Ct Imaging ◽

Learning Method ◽

Learning Methods ◽

X Ray ◽

Machine Learning Methods ◽

Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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