Fuzzy Feature Representation with Bidirectional Long Short-Term Memory for Human Activity Modelling and Recognition

Mohmed, Gadelhag; Adama, David Ada; Lotfi, Ahmad

doi:10.1007/978-3-030-29933-0_2

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…As the LSTM can only get the information from the previous iteration, a further improvement was made to the existed LSTM to give the Bidirectional LSTM (Bi-LSTM) model [14]. Bi-LSTM is the extended version of the standard LSTM which is a combination of two -forward and backward-LSTMs, so it has the ability to handle information from both future and past iteration [10].…”

Section: Bidirectional Long Short-term Memorymentioning

confidence: 99%

Using AI Approaches for Predicting Tomato Growth in Hydroponic Systems

Mohmed

Grundy

Lotfi

et al. 2021

Advances in Intelligent Systems and Computing

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In any hydroponic system, effective plant growth and yield prediction are important in precision farming. Improving model growth systems can improve plant growth conditions for enhanced crop production and better quality food and increase resource use efficiency (e.g. light use efficiency, fertiliser use efficiency, water use efficiency) that meets the market demand with lower costs. Recently, different Artificial Intelligence (AI) techniques include machine learning and, deep learning methods are employed for providing powerful analytical tools. The proposed research in this study uses different machine/deep learning techniques to predict yield and plant growth variation for tomato crop (Tiny Tim mini tomato) under three different experimental conditions. The three tomato plants were grown under 3 different light treatments. The number of yielded fruits is predicted depends on the environmental conditions in each light treatment. This research deploys deep learning techniques includes Bidirectional Long short-term Memory (Bi-LSTM) with an attention mechanism for punctuation restoration and a standard Long Short-Term Memory (LSTM) recurrent neuron network. As well as, some other machine learning techniques including Support Vector Machine (SVM) and Random Forest (RF) for predicting the tomato yield. In the three different treatments, the tomato fruits yield growth and number yielded fruits values are used by the employed AI techniques to model the targeted growth parameters. The comparative results obtained from each technique are presented and discussed utilising the cross-validation process, to evaluate the performance achieved by the different methods. Very promising results, based on the data generated from the tomato growth and development with three light treatments are presented. The results that have been achieved are (97.8%) and (88.2%) achieved by employing the Bi-LSTM and LSTM algorithms, respectively.

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Section: Bidirectional Long Short-term Memorymentioning

confidence: 99%

Using AI Approaches for Predicting Tomato Growth in Hydroponic Systems

Mohmed

Grundy

Lotfi

et al. 2021

Advances in Intelligent Systems and Computing

Self Cite

View full text Add to dashboard Cite

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Optimizing Edge Computing for Activity Recognition: A Bidirectional LSTM Approach on the PAMAP2 Dataset

Bollampally,

Kavitha,

Sumanya

et al. 2024

Eng. Technol. Appl. Sci. Res.

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This study investigates the application of a Bidirectional Long Short-Term Memory (BiLSTM) model for Human Activity Recognition (HAR) using the PAMAP2 dataset. The aim was to enhance the accuracy and efficiency of recognizing daily activities captured by wearable sensors. The proposed BiLSTM-based model achieved outstanding performance, with 98.75% training accuracy and 99.27% validation accuracy. It also demonstrated high precision, recall, and F1 scores (all 0.99). Comparative analysis with state-of-the-art models, including Deep-HAR and CNN-BiLSTM-BiGRU, revealed that the proposed BiLSTM model surpassed their performance. These results highlight the potential of the proposed approach for real-time HAR applications in edge computing, particularly where accurate and efficient activity recognition is crucial.

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Fuzzy Feature Representation with Bidirectional Long Short-Term Memory for Human Activity Modelling and Recognition

Cited by 2 publications

References 18 publications

Using AI Approaches for Predicting Tomato Growth in Hydroponic Systems

Using AI Approaches for Predicting Tomato Growth in Hydroponic Systems

Optimizing Edge Computing for Activity Recognition: A Bidirectional LSTM Approach on the PAMAP2 Dataset

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