Semantic drift: the gradual change in word meaning over time, poses a critical challenge to natural language understanding in diachronic corpora. Existing methods detect drift via alignment or distance metrics but lack the ability to model its dynamics or forecast future semantic change. We present Koopman Spectral Dynamics, the first framework to model semantic drift as a nonlinear dynamical system in word embedding space, linearly lifted via the Koopman operator. This enables interpretable forecasting, spectral decomposition, and drift attribution using Koopman eigenfunctions and modes. Applied to the HistWords: All English dataset (1800s-1990s), our method outperforms strong baselines: PCA, LSTM, and Pro-crustes, with up to 24.5% lower RMSE and 19.2% lower MAE in word forecasting, and +7.8 % accuracy in semantic coherence classification. We analyze drift trajectories using spectral energy curves, degradation plots, and mode attributions, and introduce causal influence maps via Granger and mutual information matrices. Our model generalizes to multi-word phrases (e.g., gay marriage) and supports a real-time interactive forecasting tool. This work introduces Koopman operator theory to NLP and data mining, offering a principled, spectral foundation for modeling and interpreting meaning evolution over time.

Recognizing land use and land cover elements in satellite imagery is an important foundational point for environmental tracking applications, as well as urban development and sustainable development projects. This research demonstrates the implementation of a (hybrid ConvNeXt+LNN) deep learning systems that study EuroSAT multispectral optical satellite images to carry out a classification task. The proposed model combination of convolutional feature extraction together with advanced representation learning established superior class separability for detecting the ten different land use and land cover categories. In this, model maintained excellent performance levels were maintained through 50 epochs has achieving a training accuracy of 98.79% with training loss of 0.0350 and validation accuracy 97.41% with validation loss 0.0793, leading to a test accuracy of 97.26% with a test loss of 0.0815. The model received standard classification metrics assessments and confusion matrix analysis to produce evaluation outcomes that examined per-class precision and recall, together with F1-score metrics. ROC curve analysis delivered substantial Area Under the Curve (AUC) results during the complete class examination phase.t-SNE and UMAP were applied to the high-dimensional feature vectors to visualize the learned representations, revealing well-separated clusters for each class. This indicates that the hybrid ConvNeXt+LNN model effectively captures class-specific feature distributions in latent space, enabling robust discrimination across all ten land use and land cover categories. Compared to state-of-the-art baseline models, the proposed ConvNeXt + LNN model achieves a higher test accuracy of 97.26% while maintaining enhanced computational efficiency, requiring only 625.79 seconds per epoch and a reduced parameter count (7.8M vs. 23.5M in ResNet-50). The final research demonstrates validation of the developed approach as an effective and scalable satellite-based LULC classification solution that supports geospatial systems that can be utilized for the National Geospatial Mission and United Nations Sustainable Development Goals.

Abstract The research paper presents the heterogeneous computing model for analysis & restoration of human walking deformity and posture instability. Gait‐related walking activities are very important for the analysis of postural instability, repairment of gait abnormality, diagnosis of cognitive declination, enhance the cognitive ability of human‐centered humanoid robot system, and many clinical diagnoses, for example, Parkinson, pathological gait, freezing of gait, etc. at an early stage. For experiment analysis, 10 different lower limb activities are being considered of healthy and crouch walking subjects. A total of 25 healthy and 10 crouch walk subjects are considered for experiment purposes of different age groups, sex, and mental status. To achieve this objective the pattern of 10 different rehabilitation activities are captured using RGB‐Depth (RGB‐D) camera and classified using heterogeneous deep learning models. Different deep learning models Convolutional Neural Network (CNN) and CNN‐LSTM (CNN‐Long Short Term Memory) are used for the classification of these rehabilitation exercises. The RGB‐D data is obtained using a Microsoft Kinect v2 sensor on a 100 Hz sampling frequency. Experimental results have shown significant activity recognition accuracy with 96% and 98% for CNN and CNN‐LSTM models respectively.