In autonomous driving, Outdoor Scene (OS) understanding in unfavorable weather conditions is pivotal for enabling safe navigation. Conventional studies failed to capture the intricate pairwise object relationship within scenes in OS classification. To mitigate these challenges, this research introduces a dual framework bringing together a Panoptic Cycle-Generative Adversarial Network (PC-GAN) with a Transfer Learning-based Squeeze-and-Zero-Knowledge Spatial Excitation DenseNet (TL-SZKSE-DenseNet). The suggested PC-GAN achieves realistic cross-weather image translation with semantically aligned representations in clear and degraded scenes. The TL-SZKSE-DenseNet improves feature learning with the addition of a zero-knowledge spatial excitation mechanism that selectively enhances weather-independent and context-dependent features for enhanced discrimination in crowded scenes. This generative–discriminative hybrid method effectively overcomes structured and unstructured driving domain gaps, as demonstrated on benchmark datasets such as BDD and IndiaScene365. Primarily, the OS images are generalized to adverse conditions using Panoptic Cycle-Generative Adversarial Network (PC-GAN). After that, using saliency mapping, the most significant regions in the generalized output are highlighted. In the meantime, the significant features are extracted utilizing the Orangutan Optimization Algorithm (OOA). In addition, from the detected objects, the Object Frequency and Relationship (OFR) is analyzed. Later, using TL-SZKSE-DenseNet, the OS are classified based on the OFR and the selected features. The performance of our proposed approach was tested on our newly developed IndiaScene365 dataset as well as various benchmark datasets such as BDD100k. Our newly developed dataset, IndiaScene365, includes 3000 images that entail three different weather conditions, which are foggy weather, rainy weather, and poor light conditions along with clear day weather. The proposed approach records 94.87% accuracy, 93.24%, and 94.18% sensitivity on our dataset.

Accurate stock price prediction is crucial for financial markets, where investors and analysts forecast future prices to support informed decision-making. In this study, various methods for integrating two advanced time series prediction models, Gated Recurrent Unit (GRU) and Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS), are explored to enhance stock price prediction accuracy. GRU is recognized for its ability to capture temporal dependencies in sequential data, while N-BEATS is known for handling complex trends and seasonality components. Several integration techniques, including feature fusion, residual learning, Ensemble learning and hybrid modeling, are proposed to leverage the strengths of both models and improve forecasting performance. These methods are evaluated on datasets of ten stocks from the S&P 500, with some exhibiting strong seasonal or cyclic patterns and others lacking such characteristics. Results demonstrate that the integrated models consistently outperform individual models. Feature selection, including the integration of technical indicators, is employed during data processing to further improve prediction accuracy.

The intricate realm of time series prediction using stock market datasets from the NSE India is delved into by this research. The supremacy of LSTM architecture for forecasting in time series is initially affirmed, only for a paradigm shift to be encountered when exploring various LSTM variants across distinct sectors on the NSE (National Stock Exchange) of India. Prices of various stocks in five different sectors have been predicted using multiple LSTM model variants. Contrary to the assumption that a specific variant would excel in a particular sector, the Gated Recurrent Unit (GRU) emerged as the top performer, prompting a closer examination of its limitations and subsequent enhancement using technical indicators. The ultimate objective is to unveil the most effective model for predicting stock prices in the dynamic landscape of NSE India.