A Hybrid CNN-LSTM Model for Improving Accuracy of Movie Reviews Sentiment Analysis
Sign Up to like & getrecommendations! Published in 2019 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-019-07788-7
Abstract: Nowadays, social media has become a tremendous source of acquiring user’s opinions. With the advancement of technology and sophistication of the internet, a huge amount of data is generated from various sources like social blogs,… read more here.
Keywords: cnn lstm; lstm model; model; hybrid cnn ... See more keywords
An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China
Sign Up to like & getrecommendations! Published in 2021 at "Agricultural and Forest Meteorology"
DOI: 10.1016/j.agrformet.2021.108629
Abstract: Abstract Crop growth condition and production play an important role in food management and economic development. Therefore, estimating yield accurately and timely is of vital importance for regional food security. The long short-term memory (LSTM)… read more here.
Keywords: network; wheat yield; lstm model; model ... See more keywords
Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)
Sign Up to like & getrecommendations! Published in 2020 at "Agricultural Water Management"
DOI: 10.1016/j.agwat.2020.106386
Abstract: Abstract As the standard method to compute reference evapotranspiration (ET0), Penman-Monteith (PM) method requires eight meteorological input variables, which makes it difficult to apply in data scarce regions. To overcome this problem, a hybrid bi-directional… read more here.
Keywords: term; lstm model; term daily; day ... See more keywords
The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases
Sign Up to like & getrecommendations! Published in 2021 at "Infectious Disease Modelling"
DOI: 10.1016/j.idm.2021.12.005
Abstract: The coronavirus disease that outbreak in 2019 has caused various health issues. According to the WHO, the first positive case was detected in Bangladesh on 7th March 2020, but while writing this paper in June… read more here.
Keywords: lstm model; deep learning; recovered death; confirmed recovered ... See more keywords
Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations
Sign Up to like & getrecommendations! Published in 2020 at "Renewable Energy"
DOI: 10.1016/j.renene.2020.05.150
Abstract: Abstract Accurate short-term solar irradiance forecasting is crucial for ensuring the optimum utilization of photovoltaic power generation sources. This study addresses this issue by proposing a spatiotemporal correlation model based on deep learning. The proposed… read more here.
Keywords: lstm model; irradiance; cnn lstm; short term ... See more keywords
Evaluating and clustering retrosynthesis pathways with learned strategy†
Sign Up to like & getrecommendations! Published in 2020 at "Chemical Science"
DOI: 10.1039/d0sc05078d
Abstract: With recent advances in the computer-aided synthesis planning (CASP) powered by data science and machine learning, modern CASP programs can rapidly identify thousands of potential pathways for a given target molecule. However, the lack of… read more here.
Keywords: lstm model; retrosynthesis pathways; patent extracted; tree lstm ... See more keywords
Evaluating different deep learning models for efficient extraction of Raman signals from CARS spectra
Sign Up to like & getrecommendations! Published in 2023 at "Physical Chemistry Chemical Physics"
DOI: 10.1039/d3cp01618h
Abstract: The nonresonant background (NRB) contribution to the coherent anti-Stokes Raman scattering (CARS) signal distorts the spectral line shapes and thus degrades the chemical information. Hence, finding an effective approach for removing NRB and extracting resonant… read more here.
Keywords: neural network; different deep; cars spectra; lstm model ... See more keywords
Towards high-accuracy classifying attention-deficit/hyperactivity disorders using CNN-LSTM model
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/ac7f5d
Abstract: Objective. The neurocognitive attention functions involve the cooperation of multiple brain regions, and the defects in the cooperation will lead to attention-deficit/hyperactivity disorder (ADHD), which is one of the most common neuropsychiatric disorders for children.… read more here.
Keywords: lstm model; cnn lstm; attention deficit; model ... See more keywords
Seml: A Semantic LSTM Model for Software Defect Prediction
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2925313
Abstract: Software defect prediction can assist developers in finding potential bugs and reducing maintenance cost. Traditional approaches usually utilize software metrics (Lines of Code, Cyclomatic Complexity, etc.) as features to build classifiers and identify defective software… read more here.
Keywords: defect prediction; lstm model; prediction; software defect ... See more keywords
A Novel Temporal Feature Selection Based LSTM Model for Electrical Short-Term Load Forecasting
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3196476
Abstract: An accurate electrical Short-term Load Forecasting (STLF) is an eminent factor in the power generation, electrical load dispatching and energy planning for the power supply companies, specifically in developing countries. This paper proposes a novel… read more here.
Keywords: lstm model; model; load; electrical short ... See more keywords
Multitask LSTM Model for Human Activity Recognition and Intensity Estimation Using Wearable Sensor Data
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2020.2996578
Abstract: Human activity recognition (HAR) and measuring the intensity of activity are increasingly important for healthcare applications, such as fitness tracking and patient monitoring. However, these two tasks have been performed separately, leading to delays and… read more here.
Keywords: human activity; intensity; intensity estimation; activity ... See more keywords