scholarly journals DISTRIBUTED DEEP LEARNING FRAMEWORK FOR SMART BUILDING TRANSDUCER NETWORK

2021 ◽  
Vol 4 (2) ◽  
pp. 127-139
Author(s):  
Ivan M. Lobachev ◽  
Svitlana G. Antoshchuk ◽  
Mykola A. Hodovychenko
Keyword(s):  
Deep Learning ◽  
Operating Time ◽  
Third Party ◽  
Process Data ◽  
Moderate Amount ◽  
Human Actions ◽  
Learning Framework ◽  
Distributed Framework ◽  
Work Variations ◽  
Hardware Platforms

This work is devoted to the development of a distributed framework based on deep learning for processing data from various sensors that are generated by transducer networks that are used in the field of smart buildings. The proposed framework allows you to process data that comes from sensors of various types to solve classification and regression problems. The framework architecture consists of several subnets: particular convolutional net that handle input from the same type of sensors, a single convolutional fusion net that processes multiple outputs of particular convolutional nets. Further, the result of a single convolutional fusion net is fed to the input of a recurrent net, which allows extracting meaningful features from time sequences. The result of the recurrent net opera- tion is fed to the output layer, which generates the framework output based on the type of problem being solved. For the experimental evaluation of the developed framework, two tasks were taken: the task of recognizing human actions and the task of identifying a person by movement. The dataset contained data from two sensors (accelerometer and gyroscope), which were collected from 9 users who performed 6 actions. A mobile device was used as the hardware platforms, as well as the Edison Compute Module hardware device. To compare the results of the work, variations of the proposed framework with different architectures were used, as well as third-party approaches based on various methods of machine learning, including support machines of vectors, a random forest, lim- ited Boltzmann machines, and so on. As a result, the proposed framework, on average, surpassed other algorithms by about 8% in three metrics in the task of recognizing human actions and turned out to be about 13% more efficient in the task of identifying a per- son by movement. We also measured the power consumption and operating time of the proposed framework and its analogues. It was found that the proposed framework consumes a moderate amount of energy, and the operating time can be estimated as acceptable.

scholarly journals HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yu Li ◽  
Zeling Xu ◽  
Wenkai Han ◽  
Huiluo Cao ◽  
Ramzan Umarov ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Deep Learning ◽  
Resistance Genes ◽  
Antibiotic Resistance Genes ◽  
Third Party ◽  
Superior Performance ◽  
Beta Lactamase ◽  
Learning Framework ◽  
Sequence Encoding ◽  
Global Threat

Abstract Background The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs. Results Here, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method. Conclusions We propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/.

Modelling a Deep Learning Framework for Recognition of Human Actions on Video

2021 ◽  
pp. 104-112
Author(s):  
Flávio Santos ◽  
Dalila Durães ◽  
Francisco Marcondes ◽  
Marco Gomes ◽  
Filipe Gonçalves ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Actions ◽  
Learning Framework

scholarly journals Identification of Bacteriophages Using Deep Representation Model with Pre-training

2021 ◽  
Author(s):  
Zeheng Bai ◽  
Yao-zhong Zhang ◽  
Satoru Miyano ◽  
Rui Yamaguchi ◽  
Satoshi Uematsu ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Deep Learning ◽  
Sequence Similarity ◽  
Representation Learning ◽  
Learning Model ◽  
Third Party ◽  
Knowledge Representations ◽  
Learning Framework ◽  
Advantages And Disadvantages ◽  
Alignment Free

Bacteriophages/Phages are viruses that infect and replicate within bacteria and archaea. Antibiotic resistance is one of the biggest threats to global health. The therapeutic use of bacteriophages provides another potential solution for solving antibiotic resistance. To develop phage therapies, the identification of phages from metagenome sequences is the fundamental step. Currently, several methods have been developed for identifying phages. These methods can be categorized into two types: database-based methods and alignment-free methods. The database-based approach, such as VIBRANT, utilizes existing databases and compares sequence similarity between candidates and those in the databases. The alignment-free method, such as Seeker and DeepVirFinder, uses deep learning models to directly predict phages based on nucleotide sequences. Both approaches have their advantages and disadvantages. In this work, we propose using a deep representation learning model with pre-training to integrate the database-based and non-alignment-based methods (we call it INHERIT). The pre-training is used as an alternative way for acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compared the proposed method with VIBRANT and Seeker on a third-party benchmark dataset. Our experiments show that INHERIT achieves better performance than the database-based approach and the alignment-free method, with the best F1-score of 0.9868. Meanwhile, we demonstrated that using pre-trained models helps to improve the non-alignment deep learning model further.

scholarly journals The Use of Transformer Model in Opinion Summarisation

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 05) ◽  
pp. 1084-1095
Author(s):  
Kethan Pabbi ◽  
C. Sindhu ◽  
Isukapalli Sainath Reddy ◽  
Bhumireddy Naga Sai Abhijit
Keyword(s):  
Deep Learning ◽  
Research And Development ◽  
Accurate Assessment ◽  
Process Data ◽  
Useful Knowledge ◽  
Learning Framework ◽  
Customer Feedback ◽  
Feedback Strategies ◽  
Transformer Model ◽  
Basic Content

We live in an age of information, therefore collected data and documentation are practically treasure resources. All about a business and its development can be estimated with clarity via statistics. Any machine that could really analyse information to predict a projected outcome is known for being extremely vital for the business. It is critical for the system to provide accurate and useful knowledge of the products in order to conduct accurate assessment. Summarisation is a technique for obtaining a rundown from series of sentences in a study or observation that facilitates us with understanding the basic content of the knowledge expressed within. Simple and brief summaries of just a product will assist the system in performing prospective product research and development. In our paper, we use a deep learning framework that provides to extract clean, relevant, brief summaries from comprehensive customer feedback. Strategies of abstractive text summarisation is used. The method of extracting the primary keyword from a statement and using them in the summary is defined as extractive text summarisation. We utilise abstractive summarisation in this case, which evolves from sample information and provides the best feasible description. Utilising Transformer with Depth Scaling MultiHeaded Attention as well as GloVe word embedding with positional encoding, we illustrate an abstractive approach to extract summaries from the Amazon fine food reviews dataset. Transformer aids in the parallelisation of workloads in order to process data more quickly. We have used an Attention layer which boost the model's quality and enables it to become more effective. The BLUE rating is used to quantify the model's potency.

A Deep Learning Framework for Prediction of Retinal Disorders

2020 ◽  
Author(s):  
Raniyaharini R ◽  
Madhumitha K ◽  
Mishaa S ◽  
Virajaravi R
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
Retinal Disorders

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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