An Ensemble Deep Learning Approach to Explore the Impact of Enticement, Engagement and Experience in Reward Based Crowdfunding

AbstractClimate models (CM) are used to evaluate the impact of climate change on the risk of floods and heavy precipitation events. However, these numerical simulators produce outputs with low spatial resolution that exhibit difficulties representing precipitation events accurately. This is mainly due to computational limitations on the spatial resolution used when simulating multi-scale weather dynamics in the atmosphere. To improve the prediction of high resolution precipitation we apply a Deep Learning (DL) approach using input data from a reanalysis product, that is comparable to a climate model’s output, but can be directly related to precipitation observations at a given time and location. Further, our input excludes local precipitation, but includes model fields (weather variables) that are more predictable and generalizable than local precipitation. To this end, we present TRU-NET (Temporal Recurrent U-Net), an encoder-decoder model featuring a novel 2D cross attention mechanism between contiguous convolutional-recurrent layers to effectively model multi-scale spatio-temporal weather processes. We also propose a non-stochastic variant of the conditional-continuous (CC) loss function to capture the zero-skewed patterns of rainfall. Experiments show that our models, trained with our CC loss, consistently attain lower RMSE and MAE scores than a DL model prevalent in precipitation downscaling and outperform a state-of-the-art dynamical weather model. Moreover, by evaluating the performance of our model under various data formulation strategies, for the training and test sets, we show that there is enough data for our deep learning approach to output robust, high-quality results across seasons and varying regions.

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AUGMENTED REALITY IN FACILITATING LEARNING: A REVIEW

Asia-Pacific Journal of Information Technology and Multimedia ◽

10.17576/apjitm-2021-1001-07 ◽

2021 ◽

Vol 10 (01) ◽

pp. 74-85

Author(s):

Azmah Saat ◽

Nur Izah Ab. Razak ◽

Razif Abas ◽

Rahmita Wirza O.K. Rahmat

Keyword(s):

Deep Learning ◽

Augmented Reality ◽

Teaching And Learning ◽

Learning Approach ◽

Research Areas ◽

Learning Aids ◽

Life Long Learning ◽

Augmented Reality Application ◽

The Impact ◽

Motivate Learning

The present study reviews the augmented reality application in the education area. Justification of the use is to improve the learning motivation and outcome of students through the use of a deep learning approach. A deep learning approach is associated with better academic performances. Augmented reality provides a tool to motivate learning and understanding in the student. In the medical domain, augmented reality may serve as replacement of cadavers especially during online learning. The main aim of the current review is to reflect the use of augmented reality as a teaching and learning tool to motivate and promote the deep learning approach process which includes life-long learning and possible research areas that warrant and assess the impact of augmented reality on academic performance. Augmented reality may be a tool in complementing the learning aids of the conventional methods of teaching and learning.

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Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method

Teknika ◽

10.34148/teknika.v10i1.328 ◽

2021 ◽

Vol 10 (1) ◽

pp. 62-67

Author(s):

Faisal Dharma Adhinata ◽

Diovianto Putra Rakhmadani

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Learning Approach ◽

Short Term ◽

Term Memory ◽

Learning Techniques ◽

Long Short Term Memory ◽

The Impact

The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.

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Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease

PeerJ Computer Science ◽

10.7717/peerj-cs.369 ◽

2021 ◽

Vol 7 ◽

pp. e369

Author(s):

Arpan Srivastava ◽

Sonakshi Jain ◽

Ryan Miranda ◽

Shruti Patil ◽

Sharnil Pandya ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Respiratory Diseases ◽

Predictive Accuracy ◽

Research Work ◽

Respiratory Sounds ◽

Chronic Obstructive ◽

Learning Approach ◽

X Rays ◽

The Impact

In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.

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Deep Learning Approach to Determine the Impact of Socio Economic Factors on Bitcoin Price Prediction

2019 Twelfth International Conference on Contemporary Computing (IC3) ◽

10.1109/ic3.2019.8844928 ◽

2019 ◽

Cited By ~ 1

Author(s):

Apoorva Aggarwal ◽

Isha Gupta ◽

Novesh Garg ◽

Anurag Goel

Keyword(s):

Deep Learning ◽

Learning Approach ◽

Economic Factors ◽

Price Prediction ◽

The Impact ◽

Socio Economic Factors

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A deep learning approach for complex microstructure inference

Nature Communications ◽

10.1038/s41467-021-26565-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ali Riza Durmaz ◽

Martin Müller ◽

Bo Lei ◽

Akhil Thomas ◽

Dominik Britz ◽

...

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Electron Backscatter Diffraction ◽

Specimen Preparation ◽

Learning Approach ◽

Plausible Model ◽

Lath Bainite ◽

Image Context ◽

Visualization Techniques ◽

The Impact

AbstractAutomated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing. This, along with deep learning’s seemingly intransparent decision-making process, hampers its breakthrough in this field. We apply a multidisciplinary deep learning approach, devoting equal attention to specimen preparation and imaging, and train distinct U-Net architectures with 30–50 micrographs of different imaging modalities and electron backscatter diffraction-informed annotations. On the challenging task of lath-bainite segmentation in complex-phase steel, we achieve accuracies of 90% rivaling expert segmentations. Further, we discuss the impact of image context, pre-training with domain-extrinsic data, and data augmentation. Network visualization techniques demonstrate plausible model decisions based on grain boundary morphology.

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