Freezing of gait (FoG) is a debilitating symptom in Parkinson’s disease (PD), often leading to falls and reduced quality of life. Accurate, real-time detection and prediction of FoG events remain challenging due to patient variability, short-term onset, and noisy sensor data. This study proposes a grey wolf optimized transformer (Transformer-GWO) model for enhanced multi-class FoG event prediction using wearable sensor data from a publicly available dataset. The proposed model integrates the Transformer’s ability to capture long-range temporal dependencies with grey wolf optimizer (GWO) for hyperparameter tuning, improving classification accuracy and robustness. Comparative experiments against optimized convolutional neural network (CNN-GWO), long short-term memory (LSTM-GWO), and three recent state-of-the-art baselines demonstrate that Transformer-GWO achieves the highest accuracy (98.41%), precision (98.32%), recall (98.29%), F1-score (98.30%), and AUC (0.993), while maintaining competitive computational efficiency. Specifically, Transformer-GWO achieved a training time of 22.49 s, inference time of 2.60 s, and modest memory usage (~ 211 MB), outperforming other models in speed–accuracy balance. Detailed attention map analysis, feature importance rankings, and per-class confusion matrices illustrate the interpretability of the model and its relevance for clinical deployment. These findings suggest that Transformer-GWO can provide accurate, interpretable, and computationally efficient FoG event prediction, potentially aiding in patient monitoring and personalized PD management.

Communication between humans is impossible without the use of language. Natural Language Processing (NLP) is a technique that is used so that computers can comprehend various natural languages. Named Entity Recognition (NER) is a subtask of information extraction that aims to discover and categorize the components in given text into pre-defined categories. NER is an abbreviation for the phrase ”named entity recognition”. Machine translation, question-answering systems, and automatic summarization are exam- ples of the types of NLP tasks that can benefit from NER. The purpose of this research is to investigate whether or if it is possible to create a chatbot that converses in the Kumaon language, as well as any poten- tial difficulties that could arise in the process. In addition to this, the authors provide an in-depth exami- nation of Kumaoni as well as a mapping of the language into other languages to make its use in industrial processing more accessible.

Because of the high dimensionality, heterogeneity, and resource constraints of IoT environments, protecting data from intrusions is a critical challenge in