Electrical Load Disaggregation using a two-stage deep learning approach

Colorectal cancer is one of the most common cancers with a high mortality rate. The determination of microsatellite instability (MSI) status in resected cancer tissue is vital because it helps diagnose the related disease and determine the relevant treatment. This paper presents a two-stage classification method for predicting the MSI status based on a deep learning approach. The proposed pipeline includes the serial connection of the segmentation network and the classification network. In the first stage, the tumor area is segmented from the given pathological image using the Feature Pyramid Network (FPN). In the second stage, the segmented tumor is classified as MSI-L or MSI-H using Inception-Resnet-V2. We examined the performance of the proposed method using pathological images with 10× and 20× magnifications, in comparison with that of the conventional multiclass classification method where the tissue type is identified in one stage. The F1-score of the proposed method was higher than that of the conventional method at both 10× and 20× magnifications. Furthermore, we verified that the F1-score for 20× magnification was better than that for 10× magnification.

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A Novel Deep Learning Approach for Short and Medium-Term Electrical Load Forecasting Based on Pooling LSTM-CNN Model

2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) ◽

10.1109/icpsasia48933.2020.9208557 ◽

2020 ◽

Author(s):

Yabiao Yang ◽

Ejaz UI Haq ◽

Youwei Jia

Keyword(s):

Deep Learning ◽

Load Forecasting ◽

Learning Approach ◽

Electrical Load ◽

Medium Term ◽

Electrical Load Forecasting

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Electrical load forecasting: A deep learning approach based on K-nearest neighbors

Applied Soft Computing ◽

10.1016/j.asoc.2020.106900 ◽

2020 ◽

pp. 106900

Author(s):

Yunxuan Dong ◽

Xuejiao Ma ◽

Tonglin Fu

Keyword(s):

Deep Learning ◽

Load Forecasting ◽

Nearest Neighbors ◽

Learning Approach ◽

Electrical Load ◽

K Nearest Neighbors ◽

Electrical Load Forecasting

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A Two-Stage Transfer Learning-Based Deep Learning Approach for Production Progress Prediction in IoT-Enabled Manufacturing

IEEE Internet of Things Journal ◽

10.1109/jiot.2019.2940131 ◽

2019 ◽

Vol 6 (6) ◽

pp. 10627-10638 ◽

Cited By ~ 4

Author(s):

Shaohua Huang ◽

Yu Guo ◽

Daoyuan Liu ◽

Shanshan Zha ◽

Weiguang Fang

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Learning Approach ◽

Two Stage

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Comparison of various Activation Functions A Deep Learning Approach

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.122126 ◽

2018 ◽

Vol 6 (3) ◽

pp. 122-126

Author(s):

Mohammed Ibrahim Khan ◽

◽

Akansha Singh ◽

Anand Handa ◽

◽

...

Keyword(s):

Deep Learning ◽

Learning Approach ◽

Activation Functions

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A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/3/3 ◽

2020 ◽

Vol 17 (3) ◽

pp. 299-305 ◽

Cited By ~ 1

Author(s):

Riaz Ahmad ◽

Saeeda Naz ◽

Muhammad Afzal ◽

Sheikh Rashid ◽

Marcus Liwicki ◽

...

Keyword(s):

Deep Learning ◽

Character Recognition ◽

Data Augmentation ◽

Short Term Memory ◽

Recognition System ◽

Learning Approach ◽

Arabic Text ◽

Data Set ◽

Processing Step ◽

Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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