1. A deep learning–based system for predicting environmental health impact scores, the system comprising an input layer configured to receive environmental air quality data, a Fourier feature expansion layer configured to generate sinusoidal and cosinusoidal representations of the input data, a gated linear unit layer configured to selectively regulate feature interactions, a squeeze-and-excitation mechanism configured to adaptively recalibrate feature importance, a residual dense learning block configured to preserve information flow and stabilize training, and an output layer configured to generate a continuous health impact prediction.

2. The system as claimed in claim 1, wherein the Fourier feature expansion layer projects the input data into a higher-dimensional space using a non-trainable projection matrix to capture non-linear and periodic relationships present in environmental data.

3. The system as claimed in claim 1, wherein the gated linear unit layer comprises a dense transformation producing a gating signal that modulates feature propagation through sigmoid-based activation.

4. The system as claimed in claim 1, wherein the squeeze-and-excitation mechanism performs channel-wise feature recalibration by computing global feature statistics and dynamically scaling learned features based on their predictive relevance.

5. The system as claimed in claim 1, wherein the residual dense learning block includes one or more dense layers and a residual connection that combines transformed features with earlier representations to improve gradient propagation.

6. The system as claimed in claim 1, further comprising layer normalization configured to stabilize activation distributions and dropout configured to reduce overfitting during model training.

7. The system as claimed in claim 1, wherein the output layer produces a single continuous value corresponding to a predicted environmental health impact score.

8. A method for predicting environmental health impact scores using a deep learning architecture, the method comprising receiving environmental air quality data, performing Fourier-based feature expansion, concatenating transformed features with original input features, selectively gating feature interactions using gated linear units, adaptively recalibrating features using squeeze-and-excitation mechanisms, applying residual dense learning, and generating a continuous health impact prediction.

9. The method as claimed in claim 8, wherein the deep learning architecture is trained using gradient-based optimization with normalization and regularization to ensure stable convergence and improved generalization across diverse environmental datasets.

Description:The present invention relates generally to the field of artificial intelligence, machine learning, and data-driven computational modeling. More particularly, the invention pertains to a deep learning–based system and method for analyzing environmental air quality data and predicting corresponding health impact indicators using advanced neural network architectures.
[002] The invention specifically concerns the design and implementation of a robust prediction framework that integrates feature expansion through Fourier-based transformations, gated learning mechanisms for adaptive feature interaction, and channel-wise recalibration using squeeze-and-excitation techniques to enhance the accuracy, stability, and generalization of health impact prediction models.
[003] The disclosed system is applicable to environmental health analytics, air pollution impact assessment, public health risk forecasting, and decision-support platforms, wherein complex, non-linear, and temporally varying environmental parameters must be processed to generate reliable health-related predictive outputs.
BACKGROUND OF THE INVENTION
[004] Environmental air quality has a direct and significant impact on human health, contributing to a wide range of acute and chronic medical conditions, including respiratory disorders, cardiovascular diseases, and long-term systemic complications. Rapid urbanization, industrial emissions, vehicular pollution, and climate-related variations have increased the complexity and variability of air quality patterns, thereby necessitating accurate analytical systems capable of assessing and predicting associated health impacts.
[005] Conventional approaches for evaluating health risks from environmental data predominantly rely on statistical analysis, threshold-based indices, or rule-driven models that use limited combinations of pollutant concentrations and meteorological parameters. While such methods provide basic interpretability, they are inherently constrained in their ability to capture non-linear interactions, latent correlations, and higher-order dependencies present within multidimensional environmental datasets.
[006] With the advancement of machine learning, data-driven models such as linear regression, support vector machines, and decision-tree-based systems have been applied to air quality and health impact prediction. However, these models generally depend on handcrafted features and predefined assumptions, making them sensitive to noise, seasonal variability, and unseen environmental conditions. As a result, their predictive reliability often degrades when applied across diverse geographic regions or temporal contexts.
[007] Recent developments in deep learning have introduced neural network–based architectures for environmental data analysis, leveraging their capacity to model complex non-linear relationships. Despite these advances, many existing deep learning solutions employ standard fully connected layers and basic activation functions, which inadequately represent periodic environmental patterns and fail to dynamically prioritize critical features influencing health outcomes.
[008] Furthermore, traditional deep neural networks often process input parameters as independent or static feature vectors, without incorporating mechanisms to selectively regulate information flow among features. The absence of adaptive gating structures leads to inefficient feature utilization, increased susceptibility to overfitting, and reduced interpretability of learned representations, particularly when dealing with heterogeneous air quality indicators.
[009] Another limitation of existing architectures is their inability to adaptively recalibrate the relative importance of learned features during training and inference. Without channel-wise attention or excitation mechanisms, conventional models treat all learned features uniformly, which can dilute the influence of health-critical environmental parameters and suppress subtle yet significant signals embedded in the data.
[010] Additionally, deeper neural networks frequently suffer from training instability, vanishing or exploding gradients, and slow convergence, especially when deployed on complex environmental datasets with high dimensionality and variability. The lack of residual learning structures and normalization strategies further restricts the scalability and robustness of such systems.
[011] In view of the foregoing limitations, there exists a clear need for an improved deep learning framework that can effectively model non-linear and periodic environmental patterns, dynamically regulate feature interactions, adaptively emphasize relevant features, and maintain stable learning behavior. The present invention addresses these needs by introducing a robust, integrated neural architecture specifically designed for accurate and generalizable environmental health impact prediction.
SUMMARY OF THE INVENTION
[012] The present invention provides a robust deep learning–based system and method for predicting health impact outcomes associated with environmental air quality conditions. The invention is directed to overcoming the limitations of conventional statistical, machine learning, and deep learning approaches by introducing an integrated neural architecture capable of modeling complex, non-linear, and periodic relationships inherent in environmental datasets.
[013] In accordance with the invention, the proposed system receives multidimensional environmental input data comprising air quality and related parameters, which are initially processed through a feature expansion mechanism based on Fourier transformations. This transformation projects the input data into a higher-dimensional representational space, enabling the model to capture latent periodicity and oscillatory patterns that are not effectively represented using conventional feature mappings.
[014] The expanded Fourier-based features are combined with the original input features to form an enriched feature representation. This combined representation is then processed using gated learning units that selectively regulate the flow of information through the network. The gating mechanism enables dynamic feature interaction by amplifying informative signals while suppressing less relevant or noisy features, thereby improving learning efficiency and predictive accuracy.
[015] The invention further incorporates adaptive channel-wise recalibration using squeeze-and-excitation mechanisms. These mechanisms analyze global feature statistics and dynamically adjust the relative importance of learned features during training and inference. By emphasizing health-critical environmental parameters and attenuating insignificant features, the system achieves improved sensitivity and robustness in health impact prediction.
[016] To ensure stable deep learning and efficient gradient propagation, the architecture employs residual dense learning blocks in combination with normalization and regularization techniques. Residual connections preserve information flow across layers and mitigate issues related to vanishing gradients, while layer normalization and dropout enhance convergence stability and generalization performance.
[017] The system produces a continuous predictive output corresponding to a health impact score or index derived from the processed environmental data.
[018] The invention is capable of being trained and deployed across diverse environmental datasets and operational contexts, making it suitable for use in environmental monitoring systems, public health risk assessment platforms, and decision-support applications requiring reliable health impact forecasting.
[019] Through the synergistic integration of Fourier feature expansion, gated learning, adaptive excitation, and residual deep learning, the present invention achieves superior predictive accuracy, robustness, and generalization when compared to conventional deep learning architectures, thereby providing a technically advanced solution for environmental health impact prediction.
BRIEF DESCRIPTION OF THE DRAWINGS
[020] The accompanying figures included herein, and which form parts of the present invention, illustrate embodiments of the present invention, and work together with the present invention to illustrate the principles of the invention Figures:
[021] Figure 1, illustrates the overall architecture of the proposed deep learning model comprising input processing, Fourier feature expansion, gated learning, squeeze-and-excitation based recalibration, residual dense blocks, and output regression.
DETAILED DESCRIPTION OF THE INVENTION
[022] The present invention is described in detail hereinafter with reference to the accompanying drawings, wherein like reference numerals refer to like elements throughout the description. The detailed description is provided to enable a person skilled in the art to make and use the invention and is not intended to limit the scope of the invention in any manner.
Overall System Architecture
[023] The invention discloses a deep learning–based computational system configured to predict health impact outcomes from environmental air quality data. The system comprises a plurality of interconnected neural processing layers arranged in a sequential and residual learning architecture. The architecture is specifically designed to enhance feature expressiveness, learning stability, and prediction accuracy when handling complex, non-linear, and high-dimensional environmental datasets.
[024] The system operates by receiving normalized environmental input data, performing feature expansion and adaptive feature selection, and generating a continuous output corresponding to a predicted health impact score. The disclosed architecture integrates Fourier feature transformation, gated linear units, squeeze-and-excitation mechanisms, residual dense learning, normalization, and regularization within a unified learning framework.
Input Data Processing
[025] The system includes an input layer configured to receive a set of environmental features representing air quality parameters. The input features may include, but are not limited to, particulate matter concentrations, gaseous pollutant levels, meteorological attributes, and derived environmental indicators. Prior to being supplied to the input layer, the data is scaled or normalized to ensure consistent numerical ranges and stable learning behavior.
[026] The input layer serves as a structural entry point for the environmental data and defines the dimensionality of the feature vector. No mathematical transformation is performed at this stage, and the input data is forwarded to subsequent layers for feature enrichment and learning.
Fourier Feature Expansion
[027] Following the input layer, the environmental data is processed through a Fourier feature expansion layer configured to project the input features into a higher-dimensional representation space. The Fourier feature layer applies a linear transformation using a non-trainable projection matrix, followed by sinusoidal and cosinusoidal transformations.
[028] The generated sine and cosine components enable the system to model periodic, oscillatory, and non-linear relationships that are commonly observed in environmental and air quality data. The Fourier-transformed features provide enhanced expressiveness compared to raw input features and support improved downstream learning performance.
Feature Concatenation and Gated Learning
[029] The Fourier-expanded features are concatenated with the original input features to form an enriched feature representation. This concatenation ensures that both raw environmental signals and transformed periodic representations are simultaneously available for learning.
[030] The enriched feature vector is then processed by a gated linear unit layer. The gated linear unit performs a dense transformation that produces a pair of intermediate representations, one of which is subjected to a sigmoid-based gating function. The gated output selectively modulates the flow of feature information, allowing the system to dynamically emphasize informative features while suppressing less relevant or noisy signals.
Normalization and Regularization
[031] The output of the gated learning stage is normalized using a layer normalization mechanism. Layer normalization stabilizes the distribution of activations across features, reducing internal covariate shift and improving convergence behavior during training.
[032] A dropout layer is subsequently applied to the normalized features to mitigate overfitting. By randomly deactivating a subset of neurons during training, the dropout mechanism encourages the system to learn redundant and robust feature representations that generalize effectively to unseen data.
Dense Transformation and Squeeze-and-Excitation Mechanism
[033] The regularized features are passed through a dense transformation layer configured to enhance representational capacity through non-linear activation. This dense layer learns higher-level abstractions from the gated and normalized feature set.
[034] The invention further incorporates a squeeze-and-excitation mechanism that performs adaptive channel-wise feature recalibration. The squeeze operation computes global feature statistics, which are subsequently processed through excitation layers to generate attention weights.
[035] The excitation weights dynamically scale the feature channels, amplifying features that are most relevant to health impact prediction and attenuating features with lower predictive contribution. This adaptive recalibration improves sensitivity to critical environmental indicators.
Residual Dense Learning Block
[036] The recalibrated features are subjected to additional dense transformations, including a linear dense layer without activation. The output of this transformation is combined with an earlier feature representation via a residual connection.
[037] The residual addition preserves information from previous layers and facilitates efficient gradient propagation through the network. A non-linear activation function is applied after the residual addition to introduce further representational flexibility.
[038] The residual dense learning block enables the system to learn deeper and more abstract feature representations while mitigating issues associated with vanishing gradients and training instability.
Final Regularization and Output Generation
[039] A final dropout layer is applied to the activated residual output to further improve generalization and robustness. This stage ensures that the learned representations remain resilient to noise and variations in environmental data.
[040] The output layer comprises a single neuron configured to produce a continuous value corresponding to a predicted health impact score. The output layer operates without an activation function, making it suitable for regression-based prediction tasks.
[041] The predicted output represents a quantitative estimation of health impact derived from the processed environmental data and may be used in environmental monitoring systems, public health risk assessment platforms, and decision-support applications.
[042] While the invention has been described with reference to specific embodiments and architectural configurations, it will be understood by persons skilled in the art that various modifications, substitutions, and enhancements may be made without departing from the scope and spirit of the invention as defined by the appended claims.
[043] The foregoing detailed description demonstrates that the present invention provides a technically advanced and robust deep learning system for predicting health impact outcomes based on environmental air quality data. By integrating Fourier feature expansion, gated linear units, squeeze-and-excitation based adaptive recalibration, and residual dense learning, the invention effectively addresses the limitations of conventional statistical and deep learning approaches.
[044] The disclosed architecture enables accurate modeling of complex non-linear relationships, latent periodic patterns, and dynamic feature interactions inherent in environmental datasets. The use of normalization, regularization, and residual connections ensures stable training behavior, improved convergence, and strong generalization performance across diverse environmental conditions and datasets.
[045] The invention is scalable and adaptable, allowing for extension to additional environmental parameters, temporal sequences, and heterogeneous data sources such as satellite imagery, sensor networks, and epidemiological records. Future implementations may incorporate real-time data ingestion, temporal forecasting modules, or hybrid learning strategies combining deep learning with domain-specific knowledge models.
[046] In future embodiments, the system may be deployed as part of smart city infrastructures, public health early-warning platforms, and regulatory decision-support tools. The architecture may further be enhanced through distributed or federated learning frameworks to enable privacy-preserving, large-scale environmental health analytics across multiple geographic regions.
[047] Accordingly, the present invention establishes a foundational and extensible framework for next-generation environmental health impact prediction systems, offering significant technical advancement, industrial applicability, and long-term potential for integration into advanced environmental monitoring and public health management solutions.
, Claims:1. A deep learning–based system for predicting environmental health impact scores, the system comprising an input layer configured to receive environmental air quality data, a Fourier feature expansion layer configured to generate sinusoidal and cosinusoidal representations of the input data, a gated linear unit layer configured to selectively regulate feature interactions, a squeeze-and-excitation mechanism configured to adaptively recalibrate feature importance, a residual dense learning block configured to preserve information flow and stabilize training, and an output layer configured to generate a continuous health impact prediction.
2. The system as claimed in claim 1, wherein the Fourier feature expansion layer projects the input data into a higher-dimensional space using a non-trainable projection matrix to capture non-linear and periodic relationships present in environmental data.
3. The system as claimed in claim 1, wherein the gated linear unit layer comprises a dense transformation producing a gating signal that modulates feature propagation through sigmoid-based activation.
4. The system as claimed in claim 1, wherein the squeeze-and-excitation mechanism performs channel-wise feature recalibration by computing global feature statistics and dynamically scaling learned features based on their predictive relevance.
5. The system as claimed in claim 1, wherein the residual dense learning block includes one or more dense layers and a residual connection that combines transformed features with earlier representations to improve gradient propagation.
6. The system as claimed in claim 1, further comprising layer normalization configured to stabilize activation distributions and dropout configured to reduce overfitting during model training.
7. The system as claimed in claim 1, wherein the output layer produces a single continuous value corresponding to a predicted environmental health impact score.
8. A method for predicting environmental health impact scores using a deep learning architecture, the method comprising receiving environmental air quality data, performing Fourier-based feature expansion, concatenating transformed features with original input features, selectively gating feature interactions using gated linear units, adaptively recalibrating features using squeeze-and-excitation mechanisms, applying residual dense learning, and generating a continuous health impact prediction.
9. The method as claimed in claim 8, wherein the deep learning architecture is trained using gradient-based optimization with normalization and regularization to ensure stable convergence and improved generalization across diverse environmental datasets.