HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Abstract. Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.

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HESS Opinions: Deep learning as a promising avenue toward knowledge discovery in water sciences

10.5194/hess-2018-168 ◽

2018 ◽

Cited By ~ 1

Author(s):

Chaopeng Shen ◽

Eric Laloy ◽

Adrian Albert ◽

Fi-John Chang ◽

Amin Elshorbagy ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Knowledge Discovery ◽

Model Building ◽

Scientific Discovery ◽

Machine Learning Algorithms ◽

Data Compilation ◽

Industry Applications ◽

Versatile Tool ◽

Promising Avenue

Abstract. Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in water science has so far been gradual, but the related fields are now ripe for breakthroughs. This paper proposes that DL-based methods can open up a viable, complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data for scientists to further evaluate. Interrogative studies are invoked to interpret DL models. In addition, we lay out several opinions shared by authors: (1) deep learning may bring forth transformative progress to the field of hydrology due to its ability to assimilate big data and identify commonalities and differences; (2) The community may benefit greatly from a variety of shared datasets and open competitions; (3) Big hydrologic data can be obtained via various ways including data compilation and working with citizen scientists, which offers the co-benefits of education and stakeholder engagement; (4) Water sciences, and hydrology in particular, offer a unique set of challenges that can, in turn, stimulate advances in machine learning; and (5) An urgent need for research is hydrology-customized methods for interpreting knowledge extracted by deep learning.

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Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

10.4018/978-1-7998-8161-2.ch003 ◽

2022 ◽

pp. 27-50

Author(s):

Rajalaxmi Prabhu B. ◽

Seema S.

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Model Building ◽

Large Data ◽

Machine Learning Algorithms ◽

Large Data Sets ◽

Data Sets ◽

Learning Approaches ◽

Learning Techniques ◽

Important Challenge

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.

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A survey on big data analytics with deep learning in text using machine learning mechanisms

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.21.12398 ◽

2018 ◽

Vol 7 (2.21) ◽

pp. 335

Author(s):

R Anandan ◽

Srikanth Bhyrapuneni ◽

K Kalaivani ◽

P Swaminathan

Keyword(s):

Machine Learning ◽

Big Data ◽

Deep Learning ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

Machine Learning Algorithms ◽

Specific Information ◽

Dynamic Framework ◽

Wide Range

Big Data Analytics and Deep Learning are two immense purpose of meeting of data science. Big Data has ended up being major a tantamount number of affiliations both open and private have been gathering huge measures of room specific information, which can contain enduring information about issues, for instance, national cognizance, motorized security, coercion presentation, advancing, and healing informatics. Relationship, for instance, Microsoft and Google are researching wide volumes of data for business examination and decisions, influencing existing and future progression. Critical Learning figuring's isolate odd state, complex reflections as data outlines through another levelled learning practice. Complex reflections are learnt at a given level in setting of all around less asking for thoughts figured in the past level in the dynamic framework. An indispensable favoured perspective of Profound Learning is the examination and culture of beast measures of unconfirmed data, making it a fundamental contraption for Great Statistics Analytics where offensive data is, everything seen as, unlabelled and un-arranged. In the present examination, we investigate how Deep Learning can be used for keeping an eye out for some essential issues in Big Data Analytics, including removing complex cases from Big volumes of information, semantic asking for, information naming, smart data recovery, and streamlining discriminative errands .Deep learning using Machine Learning(ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the front line as of late mostly attributable to the advert of huge information. ML counts have never been remarkable ensured while tried by gigantic data. Gigantic data engages ML counts to uncover more fine-grained cases and make more advantageous and correct gauges than whenever in late memory with deep learning; on the other hand, it exhibits genuine challenges to deep learning in ML, for instance, show adaptability and appropriated enlisting. In this paper, we introduce a framework of Deep learning in ML on big data (DLiMLBiD) to guide the discussion of its opportunities and challenges. In this paper, different machine learning algorithms have been talked about. These calculations are utilized for different purposes like information mining, picture handling, prescient examination, and so forth to give some examples. The fundamental favourable position of utilizing machine learning is that, once a calculation realizes what to do with information, it can do its work consequently. In this paper we are providing the review of different Deep learning in text using Machine Learning and Big data methods.

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.31232/osf.io/4pxq2 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Ferdinand Filip ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

State Of The Art ◽

Science Methods ◽

Learning Models ◽

Diverse Range ◽

Hybrid Machine ◽

Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

SSRN Electronic Journal ◽

10.2139/ssrn.3711309 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

Learning Methods ◽

Comprehensive Review

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Semantic segmentation of PolSAR image data using advanced deep learning model

Scientific Reports ◽

10.1038/s41598-021-94422-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rajat Garg ◽

Anil Kumar ◽

Nikunj Bansal ◽

Manish Prateek ◽

Shashi Kumar

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Urban Area ◽

Urban Areas ◽

Learning Algorithms ◽

Semantic Segmentation ◽

Learning Model ◽

Machine Learning Algorithms ◽

Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01588-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kinshuk Sengupta ◽

Praveen Ranjan Srivastava

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Image Classification ◽

Machine Learning Algorithms ◽

Classification Task ◽

Clinical Image ◽

Prototype Model ◽

Learning Models ◽

Accuracy Measure ◽

Quantum Machine Learning

Abstract Background In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. Methods This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. Results The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. Conclusion The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.

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Reviewing the relationship between machines and radiology: the application of artificial intelligence

Acta Radiologica Open ◽

10.1177/2058460121990296 ◽

2021 ◽

Vol 10 (2) ◽

pp. 205846012199029

Author(s):

Rani Ahmad

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Health Care Professionals ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Health Science ◽

Computer Algorithms ◽

Learning Models ◽

Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

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Pervasive Lying Posture Tracking

Sensors ◽

10.3390/s20205953 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5953 ◽

Cited By ~ 1

Author(s):

Parastoo Alinia ◽

Ali Samadani ◽

Mladen Milosevic ◽

Hassan Ghasemzadeh ◽

Saman Parvaneh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Computational Models ◽

Learning Algorithms ◽

Pressure Sensors ◽

Machine Learning Algorithms ◽

Sensor System ◽

Accurate Detection ◽

Research Questions ◽

Posture Tracking

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

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Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

Future Internet ◽

10.3390/fi13030067 ◽

2021 ◽

Vol 13 (3) ◽

pp. 67

Author(s):

Eric Hitimana ◽

Gaurav Bajpai ◽

Richard Musabe ◽

Louis Sibomana ◽

Jayavel Kayalvizhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Multivariate Time Series ◽

Machine Learning Algorithms ◽

Series Data ◽

Support Vector ◽

Human Beings ◽

Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

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