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This paper presents a bi-directional Long ShortTerm Memory (LSTM) model for the detection of landslides. Previous uses of machine learning in this setting have demonstrated its general potential, which necessitates the implementation of a suitable algorithm. Landslides are natural disasters that can cause significant destruction and disruption in the affected areas. Early detection is the key to minimizing the impact of landslides, so it is important to develop accurate and efficient models.
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1621 Views
The dataset consists of measurements of four different stages of degradation in low-voltage contactors used for industrial purposes. The measurements were obtained with fiber Bragg grating (FBG) sensors that detect the dynamic deformation generated in switching under different internal components. The measurements were processed and features from PSD, FFT and TSFEL python library were extracted. The features of PSD and FFT were acquired in 40 sliding windows of 50Hz from the signal.
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139 Views
Dataset1
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317 Views
These are the daily closing prices of three stock indices including Shanghai Securities Composite Index (SSEC), the Shenzhen Securities Component Index (SZI) and the Standard & Poor 500 Index (SPX). The data is obtained from Yahoo Finance (https://finance.yahoo.com) and collated. The SSEC data is from December 19,1990 to May 25, 2023. The data of SZI is from April 3, 1991 to May 25, 2023, and the data of SPX is from December 3, 1990 to May 25, 2023.
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42 Views
During our research in generating or optimizing molecules to be drug candidates by extending deep reinforcement learning and graph neural networks algorithms, we used GEOM data [1], and we had an idea to make a dataset obtained from molecules from GEOM to predit the activity towards COVID and the drug linkeness. We calculated over 200 descriptors for the molecules using RDKit [2]. We hope you enjoy using it.
References:
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372 Views
<p>This dataset consists of 200 occurrences extracted from three fields of a web analytics tool over time, along with labels indicating the service availability status at that moment. The data pertains to customer accesses of a real financial institution. The columns are named with a type and a unique identifier number. The column TX_ACAO_EVT represents an action performed by the customer, such as a click, system message, or background application action. The column TX_CTGR_EVT represents the category of the action, such as an error message or a specific type of action.
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308 Views
The datasets in discussion present detailed records for two of the world's most cultivated crops: wheat and rice. These datasets aim to provide comprehensive insights into various environmental and soil-related factors that are traditionally considered influential in determining the yield of these crops. By analyzing these datasets, researchers, agronomists, and farmers can gain a better understanding of the interplay between different attributes and how they might impact the overall crop yield.
Wheat Dataset:
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674 Views
This dataset was derived from a questionnaire survey, including online surveys and field surveys, to test the reliability and validity of the media convergence perception scale. The questionnaire consisted of two parts. The first part was related to the 38 items generated in the prior step. The socio-demographic characteristic information of interviewees was captured in the second part. In addition, there was an additional question about whether respondents have used CLCM before, which is essential for the following data analysis.
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175 Views
A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient.
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467 Views
Abstract- Cardiovascular diseases (CVDs) remain a sig- nificant global health challenge, emphasizing the critical need for accurate predictive models to address early detec- tion and intervention. This study presents a comprehensive framework for heart disease prediction using advanced ma- chine learning techniques.
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2655 Views