scholarly journals A Bidirectional Deep-Learning-Based Spectral Attention Mechanism for Hyperspectral Data Classification

2022 ◽  
Vol 14 (1) ◽  
pp. 217
Author(s):  
Bishwas Praveen ◽  
Vineetha Menon
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Short Term Memory ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Attention Mechanism ◽  
Spectral Information ◽  
Analysis Model ◽  
Analysis Techniques ◽  
Spectral Bands

Hyperspectral remote sensing presents a unique big data research paradigm through its rich information captured across hundreds of spectral bands, which embodies vital spatial and temporal information about the underlying land cover. Deep-learning-based hyperspectral data analysis methodologies have made significant advancements over the past few years. Despite their success, most deep learning frameworks for hyperspectral data classification tend to suffer in terms of computational and classification efficacy as the data size increases. This is largely due to their equal emphasis criteria on the rich spectral information present in the data, albeit all of the spectral information not being essential for hyperspectral data analysis. On the contrary, this redundant information present in the spectral bands can deter the performance of hyperspectral data analysis techniques. Therefore, in this work, we propose a novel bidirectional spectral attention mechanism, which is computationally efficient and capable of adaptive spectral information diversification through selective emphasis on spectral bands that comprise more information and suppress the ones with lesser information. The concept of 3D-convolutions in tandem with bidirectional long short-term memory (LSTM) is used in the proposed architecture as spectral attention mechanism. A feedforward neural network (FNN)-based supervised classification is then performed to validate the performance of our proposed approach. Experimental results reveal that the proposed hyperspectral data analysis model with spectral attention mechanism outperforms other spatial- and spectral-information-extraction-based hyperspectral data analysis techniques compared.

scholarly journals Analisis Keserasian Belanja Daerah Kabupaten Garut

2018 ◽  
Vol 10 (2) ◽  
pp. 87-95
Author(s):  
Abdal Abdal ◽  
Herabudin Herabudin ◽  
Siti Saodah
Keyword(s):  
Data Analysis ◽  
Qualitative Approach ◽  
Fiscal Year ◽  
Capital Expenditures ◽  
Analysis Model ◽  
Analysis Techniques ◽  
Three Stages ◽  
Operating Expenditures ◽  
Descriptive Method ◽  
Year 2013

The problem in this study relates to the level of compatibility of operating expenditures, capital expenditures as well as direct and indirect expenditure in the Budget Realization Report (LRA) Garut district fiscal year 2013-2017. The aim of this study was to determine the level of compatibility of operating expenditures, capital expenditures, as well as direct and indirect expenditure on Budget Realization Report (BRR) Garut regency Fiscal Year 2013-2017. The method used in this research is descriptive method with qualitative approach. Data collection techniques in this study is the observation, documentation, interviews and triangulation. Data analysis techniques in this study is an analysis model of Miles and Huberman which consists of three stages: 1) Reduction of data, 2) data, and 3) conclusion / verification. The result is the expenditures to Garut regency 2013-2017 fiscal year quite well.

scholarly journals CLASSIFICATION OF HYPERSPECTRAL DATA BASED ON GUIDED FILTERING AND RANDOM FOREST

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 821-824
Author(s):  
H. Ma ◽  
W. Feng ◽  
X. Cao ◽  
L. Wang
Keyword(s):  
Random Forest ◽  
Infrared Imaging ◽  
Image Filtering ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Data Set ◽  
Imaging Spectrometer ◽  
Guided Image Filter ◽  
Spectral Bands ◽  
Guided Image Filtering

Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to “the curse of dimensionality”. In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA), guided image filtering and the random forest classifier (RF). In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.

scholarly journals PENGARUH PENGETAHUAN DAN MOTIVASI KERJA PPKBD TERHADAP KINERJA DAN KETAHANAN KELUARGA

2019 ◽  
Vol 20 (1) ◽  
pp. 1-9
Author(s):  
Willy Faisal ◽  
Ahmad Zubaidi ◽  
Hakimul Ikhwan
Keyword(s):  
Data Analysis ◽  
Family Planning ◽  
Work Motivation ◽  
Descriptive Analysis ◽  
Family Resilience ◽  
Analysis Model ◽  
Analysis Techniques ◽  
Rural Family ◽  
Inferential Analysis ◽  
Path Analysis Model

The aim of this research are (1) to determine the effect of knowledgeon the performance of rural family planning officer of Semarang District (PPKBD) (2) to determine the effect of work motivation on the performance of Semarang District PPKBD (3) to deter mine the influence of knowledge on Family Resilience in Semarang District (4) to determine the effect of work motivation towards Family Resilience in Semarang District (5) to determine the effect of PPKBD's performance on Family Resilience in Semarang Regency. The population used in this study were all members of the Semarang District PPKBD. Based on the sample calculation, the resultsobtained were 148 members of PPKBD as respondents. Data Analysis Techniques used are Descriptive Analysis, Inferential Analysis, and Path Analysis Model Testing. Based on the results of the testing revealed that all hypothesis were accepted

scholarly journals Depth and Width Changeable Network-Based Deep Kernel Learning-Based Hyperspectral Sensor Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Liu ◽  
Tingting Wang ◽  
Yulong Qiao
Keyword(s):  
Data Analysis ◽  
Network Architecture ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Kernel Learning ◽  
Discriminative Ability ◽  
Neural Network Learning ◽  
And Performance ◽  
Hyperspectral Sensor

Sensor data analysis is used in many application areas, for example, Artificial Intelligence of Things (AIoT), with the rapid developing of the deep neural network learning that promotes its application area. In this work, we propose the Depth and Width Changeable Deep Kernel Learning-based hyperspectral sensing data analysis algorithm. Compared with the traditional kernel learning-based hyperspectral data classification, the proposed method has its advantages on the hyperspectral data classification. With the deep kernel learning, the feature is mapped through many times mapping and has the more discriminative ability. So, the deep kernel learning has the better performance compared with the multiple kernels learning. And it has the ability to adjust the network architecture for hyperspectral data space, with the optimization equation of the span bound. The experiments are implemented to testified the feasibility and performance of the algorithms on the hyperspectral data analysis, with the classification accuracy of hyperspectral data. The comprehensive analysis of the experiments shows that the proposed algorithm is feasible to hyperspectral sensor data analysis and its promising classification method in many areas data analysis.

scholarly journals Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach

2021 ◽  
Vol 2 ◽  
Author(s):  
Yongliang Qiao ◽  
Cameron Clark ◽  
Sabrina Lomax ◽  
He Kong ◽  
Daobilige Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Video Data ◽  
Identification Accuracy ◽  
Sequence Length ◽  
Livestock Farming ◽  
Precision Livestock Farming ◽  
Learning Framework ◽  
Spatio Temporal

Individual cattle identification is a prerequisite and foundation for precision livestock farming. Existing methods for cattle identification require radio frequency or visual ear tags, all of which are prone to loss or damage. Here, we propose and implement a new unified deep learning approach to cattle identification using video analysis. The proposed deep learning framework is composed of a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a self-attention mechanism. More specifically, the Inception-V3 CNN was used to extract features from a cattle video dataset taken in a feedlot with rear-view. Extracted features were then fed to a BiLSTM layer to capture spatio-temporal information. Then, self-attention was employed to provide a different focus on the features captured by BiLSTM for the final step of cattle identification. We used a total of 363 rear-view videos from 50 cattle at three different times with an interval of 1 month between data collection periods. The proposed method achieved 93.3% identification accuracy using a 30-frame video length, which outperformed current state-of-the-art methods (Inception-V3, MLP, SimpleRNN, LSTM, and BiLSTM). Furthermore, two different attention schemes, namely, additive and multiplicative attention mechanisms were compared. Our results show that the additive attention mechanism achieved 93.3% accuracy and 91.0% recall, greater than multiplicative attention mechanism with 90.7% accuracy and 87.0% recall. Video length also impacted accuracy, with video sequence length up to 30-frames enhancing identification performance. Overall, our approach can capture key spatio-temporal features to improve cattle identification accuracy, enabling automated cattle identification for precision livestock farming.

scholarly journals ANALISIS TINDAK ILOKUSI YANG DILAKUKAN OLEH GSD DALAM VIDEO KENAPA KITA MEMBENCI?

2021 ◽  
Vol 22 (1) ◽  
pp. 41
Author(s):  
Suci Nurul Afidah ◽  
Asep Purwo Yudi Utomo
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Qualitative Method ◽  
Data Classification ◽  
Analysis Techniques ◽  
Illocutionary Acts ◽  
Data Source ◽  
Data Inference ◽  
Descriptive Qualitative

The purpose of this research is to describe the illocutionary acts on one of Gita Savitri Devi’s Youtube Channel video entitled Kenapa kita membenci? Beropini eps. 46. The method use in this research is descriptive qualitative method. The data in this research are illocutionary acts spoken by Gita Savitri Devi in one of her Youtube Channel videos. The data source in this research is the narration delivered by Gita Savitri Devi in that video. The data collection techniques using hearken technique. Data analysis techniques in this research were carried out through the steps : (1) Data transcription, (2) Data classification, and (3) Data inference. The results obtained by the type of assertive illocutionary acts, directive illocutionary acts, and expresive illocutionary acts. While the types of commissive illocutionary acts and declaration illocutionary acts were not found in this research.

scholarly journals Deep Learning Methods In Predicting Indonesia Composite Stock Price Index (IHSG)

2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Arief Fadhlurrahman Rasyid ◽  
Dewi Agushinta R. ◽  
Dharma Tintri Ediraras
Keyword(s):  
Deep Learning ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Analysis Model ◽  
Accuracy Rate ◽  
Learning Methods ◽  
Stock Price Index

The stock price changes at any time within seconds. The stock price is a time series data. Thus, it is necessary to have the best analysis model in predicting the stock price to make decisions to avoid losses in investing. In this research, the method used two models Deep Learning namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting Indonesia Composite Stock Price Index (IHSG). The dataset used is historical data from the Jakarta Composite Index (^JKSE) stock price in 2013-2020 obtained through Yahoo Finance. The results suggest that Deep learning methods with LSTM and GRU models can predict Indonesia Composite Stock Price Index (IHSG). Based on the test results obtained RMSE value of 71.28959454502723 with an accuracy rate of 92.39% for LSTM models and obtained RMSE value of 70.61870739073838 with an accuracy rate of 96.77% on GRU models.

scholarly journals DeepHTTP: Anomalous HTTP Traffic Detection and Malicious Pattern Mining Based on Deep Learning

2020 ◽  
pp. 141-161
Author(s):  
Yuqi Yu ◽  
Hanbing Yan ◽  
Yuan Ma ◽  
Hao Zhou ◽  
Hongchao Guan
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Pattern Mining ◽  
Short Term Memory ◽  
Attack Detection ◽  
Attention Mechanism ◽  
Discriminative Ability ◽  
Feature Extraction Method ◽  
Detection Model ◽  
Traffic Detection

AbstractHypertext Transfer Protocol (HTTP) accounts for a large portion of Internet application-layer traffic. Since the payload of HTTP traffic can record website status and user request information, many studies use HTTP protocol traffic for web application attack detection. In this work, we propose DeepHTTP, an HTTP traffic detection framework based on deep learning. Unlike previous studies, this framework not only performs malicious traffic detection but also uses the deep learning model to mine malicious fields of the traffic payload. The detection model is called AT-Bi-LSTM, which is based on Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism. The attention mechanism can improve the discriminative ability and make the result interpretable. To enhance the generalization ability of the model, this paper proposes a novel feature extraction method. Experiments show that DeepHTTP has an excellent performance in malicious traffic discrimination and pattern mining.

Hapke Data Augmentation for Deep Learning-Based Hyperspectral Data Analysis With Limited Samples

2020 ◽  
pp. 1-5
Author(s):  
Kai Qin ◽  
Fangyuan Ge ◽  
Yingjun Zhao ◽  
Ling Zhu ◽  
Ming Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Data Augmentation ◽  
Hyperspectral Data
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