Improvement and Application of Multi-layer LSTM Algorithm Based on Spatial-Temporal Correlation

Zhao, Yanming

doi:10.18280/isi.250107

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…The LSTM layer, consisting of 256 units, provides the network's memory function, capturing long-term dependencies and patterns over time, making it highly relevant for analyzing the chemical structure of organic compounds and their biodegradability. The layer can be mathematically represented as [20][21][22]: 2 where f t , i t , and o t are the forget, input, and output gates, c t is the cell state, h t is the hidden state, σ is the sigmoid activation function, and ⊙ represents elementwise multiplication.…”

Section: Prediction Model Buildingmentioning

confidence: 99%

Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery

Gbadago,

Hwang,

Lee

et al. 2024

Preprint

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The increasing global demand for eco-friendly products is driving innovation in sustainable chemical synthesis, particularly the development of biodegradable substances. Herein, a novel method utilizing artificial intelligence (AI) to predict the biodegradability of organic compounds is presented, overcoming the limitations of traditional prediction methods that rely on laborious and costly density functional theory (DFT) calculations. We propose leveraging readily available molecular formulas and structures represented by simplified molecular-input line-entry system (SMILES) notation and molecular images to develop an effective AI-based prediction model using state-of-the-art machine learning techniques, including deep convolutional neural networks (CNN) and long-short term memory (LSTM) learning algorithms, capable of extracting meaningful molecular features and spatiotemporal relationships. The model is further enhanced with reinforcement learning (RL) to better predict and discover new biodegradable materials by rewarding the system for identifying unique and biodegradable compounds. The combined CNN-LSTM model achieved an 87.2% prediction accuracy, outperforming CNN- (75.4%) and LSTM-only (79.3%) models. The RL-assisted generator model produced approximately 60% valid SMILES structures, with over 80% being unique to the training dataset, demonstrating the model's capability to generate novel compounds with potential for practical application in sustainable chemistry. The model was extended to develop novel electrolytes with desired molecular weight distribution.

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Section: Prediction Model Buildingmentioning

confidence: 99%

Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery

Gbadago,

Hwang,

Lee

et al. 2024

Preprint

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show abstract

“…In 2020, Zhao [13,14] improved the SFA algorithm by multiple long short-term memory networks (LSTMs) and spatiotemporal correlations, implemented the algorithm to dynamic forecast of particulate matter 25 (PM25), and demonstrated the excellent prediction ability of the LSTMimproved algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the SFA algorithm has achieved desired effects in various fields, ranging from human behavior recognition [14][15][16], blind signal analysis [7,17], dynamic monitoring [18], three-dimensional (3D) feature extraction [19], and multi-person path planning [20]. Great progress has been realized on the theories and applications of SFA.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of a Slow Feature Algorithm Coupling Visual Selectivity and Multiple Long Short-Term Memory Networks

Zhao¹,

Yang²,

Su³

2021

Self Cite

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In the traditional slow feature analysis (SFA), the expansion of polynomial basis function lacks the support of visual computing theories for primates, and cannot learn the uniform, continuous long short-term features through selective visual mechanism. To solve the defects, this paper designs and implements a slow feature algorithm coupling visual selectivity and multiple long short-term memory networks (LSTMs). Inspired by the visual invariance theory of natural images, this paper replaces the principal component analysis (PCA) of traditional SFA algorithm with myTICA (TICA: topologically independent component analysis) to extract image invariant Gabor basis functions, and initialize the space and series of basis functions. In view of the ability of the LSTM to learn long and short-term features, four LSTM algorithms were constructed to separately predict the long and short-term visual selectivity features of Gabor basis functions from the basis function series, and combine the functions into a new basis function, thereby solving the defect of polynomial prediction algorithms. In addition, a Lipschitz consistency condition was designed, and used to develop an approximate orthogonal pruning technique, which optimizes the prediction basis functions, and constructs a hyper-complete space for the basis function. The performance of our algorithm was evaluated by three metrics and mySFA’s classification method. The experimental results show that our algorithm achieved a good prediction effect on INRIA Holidays dataset, and outshined SFA, graph-based SFA (SFA), TICA, and myTICA in accuracy and feasibility; when the threshold was 6, the recognition rate of our algorithm was 99.98%, and the false accept rate (FAR) and false reject rate (FRR) were both smaller than 0.02%, indicating the strong classification ability of our approach.

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“…In 2020, Zhao [14,15] proposed an improved multi-layer LSTM algorithm based on spatial-temporal correlation, and applied it in the dynamic prediction of PM25, which achieved good results. The study proved that: in time series analysis, the improved LSTM algorithm integrating spatial features has better long-term and short-term prediction capabilities, and can realize the spatio-temporal fusion of the prediction results, which solved the problem that the traditional LSTM algorithm could not achieve the fusion of spatial features.…”

Section: Introductionmentioning

confidence: 99%

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

Yang¹,

Zhao²,

Su³

et al. 2021

Self Cite

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The conventional slow feature analysis (SFA) algorithm has no support of computational theory of vision for primates, nor does it have the ability to learn the global features with visual selection consistency continuity. And what is more, the algorithm is highly complex. Based on this, Slow Feature Extraction Algorithm Based on Visual selection consistency continuity and Its Application was proposed. Inspired by the visual selection consistency continuity theory for primates, this paper replaced the principal component analysis (PCA) method of the conventional SFA algorithm with the myTICA method, extracted the Gabor basis functions of natural images, initialized the basis function family; it used the feature basis expansion algorithm based on visual selection consistency continuity (the VSCC_FBEA algorithm) to replace the polynomial expansion method in the original SFA algorithm to generates the Gabor basis functions of features with long and short-term visual selectivity in the family of basis functions, which solved the drawbacks of the polynomial prediction algorithm; it also designed the Lipschitz consistency constraint, and proposed the Lipschitz-Orthogonal-Pruning-Method (LOPM algorithm) to optimize the basis function family into an over-complete family of basis functions. In addition, this paper used the feature expression method based on visual invariance theory (visual invariance theory -FEM) to establish the set of features of natural images with visual selection consistency continuity. Subsequently, it adopted three error evaluation methods and mySFA classification method to evaluate the proposed algorithm. According to the experimental results, the proposed algorithm showed good prediction performance with respect to the LSVRC2012 data set; compared with the SFA, GSFA, TICA, myICA and mySFA algorithms, the proposed algorithm is correct and feasible; when the classification threshold of the algorithm was set at 8.0, the recognition rate of the proposed algorithm reached 99.66%, and neither of the false recognition rate and the false rejection rate was higher than 0.33%. The proposed algorithm has good performance in prediction and classification, and also shows good anti-noise capacity under limited noise conditions.

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Improvement and Application of Multi-layer LSTM Algorithm Based on Spatial-Temporal Correlation

Cited by 4 publications

References 35 publications

Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery

Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery

Design and Application of a Slow Feature Algorithm Coupling Visual Selectivity and Multiple Long Short-Term Memory Networks

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

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