Big Data Analytics using Deep LSTM Networks: A Case Study for Weather Prediction

Mittal, Shweta; Sangwan, Om Prakash

doi:10.25046/aj050217

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…LSTM terdiri dari sel dengan input gate, output gate dan forget gate. Dengan unit tersebut, LSTM dapat menghapus status temporal dan memungkinkan yang baru untuk disimpan atau mempertahankan status yang sama dari sebelumnya [15]. Hal ini sangat penting untuk mengklasifikasikan gerakan dinamis melalui perubahan koordinat sehingga penelitian ini menggunakan lapisan LSTM untuk modelnya.…”

Section: Long Short Term Memory (Lstm)unclassified

Prediksi Gerakan secara Real-Time untuk Pengenalan Bahasa Isyarat menggunakan LSTM

Yolanda,

Silfianti

2023

JOINTECS

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Sign Language is a language used to communicate, especially for deaf and speech impaired people so that they can communicate with each other well. However, due to the lack of education in the community, sign language is rarely used, and many do not even know sign language. So we need a system to recognize sign language so that more people understand sign language. This study developed dynamic motion detection for 6-word sign language recognition in Indonesian sign language (SIBI) by utilizing real-time coordinate changes detected using MediaPipe Holistic and LSTM Neural Network. From the results of the tests that have been carried out, it can be concluded that the results of implementing the Sign Language Recognition System in real-time by utilizing coordinate changes using the MediaPipe Holistic and Long Short Term Memory (LSTM) methods obtained a model with an accuracy value of 96%. The accuracy obtained is 96%, meaning that the model is accurate in classifying movement classes. Each class has excellent precision, recall, f1-score, and support scores with an average of 96%.

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Section: Long Short Term Memory (Lstm)unclassified

Prediksi Gerakan secara Real-Time untuk Pengenalan Bahasa Isyarat menggunakan LSTM

Yolanda,

Silfianti

2023

JOINTECS

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Metaheuristic Techniques Optimised LSTM Network for Improved Weather Prediction

Mittal,

Sangwan

2024

2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)

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Weather Forecasting using Machine Learning for Smart Farming

Prasad,

Kumar Shukla

2023

Future Farming: Advancing Agriculture With Artificial Intelligence

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Weather forecast is of prime attention of the researchers working in the smart agriculture domain. In India, approximately 55% of the total crops are dependent on weather (monsoon season). An accurate weather forecast model requires abundant data to get the most accurate predictions. However, the weather forecast is a key area of research and is always challenging from historical data. Hence, the current system used for weather forecasting is an amalgamation of forecasting models, opinions, and information trends, and specific patterns. This work presents the application of the linear regression model and polynomial regression model for weather forecasting; like a scheme to forecast rainfall, and precipitation using historical weather data. The sample weather dataset covers 75 districts of Uttar Pradesh state which is received from the Indian Meteorological Department (IMD). Furthermore, analysing the impact of forecasts with different parameters is realized over six major crops Triticum (biological name of wheat), Gram, Barley, Mustard, Sugarcane, and Maize of Uttar Pradesh State. The main objective of the state-of-the-art is efficient crop management and passing the appropriate message to farmers to make suitable decisions as per the weather conditions.

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Big Data Analytics using Deep LSTM Networks: A Case Study for Weather Prediction

Cited by 5 publications

References 13 publications

Prediksi Gerakan secara Real-Time untuk Pengenalan Bahasa Isyarat menggunakan LSTM

Prediksi Gerakan secara Real-Time untuk Pengenalan Bahasa Isyarat menggunakan LSTM

Metaheuristic Techniques Optimised LSTM Network for Improved Weather Prediction

Weather Forecasting using Machine Learning for Smart Farming

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