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The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability. [ABSTRACT FROM AUTHOR]

Due to the distinctive optical, mechanical and electrical characteristics, silicon nanowire (SilNW), one of the one dimensional nanostructures, has become a potential sensing nanomaterial. SilNWs have drawn interest in high-sensitive sensor fabrication, primarily because of their large surface-volume ratios, which significantly enhanced the detection limit to femtomolar concentrations and also provided high sensitivity. Due to its high charge sensitivity, SilNW FET-based sensors had been employed extensively for sensing various chemical as well as biological species. In this work, the sensing performance and applications of different SilNW biosensors, gas sensors, chemical and metal ion sensors were studied. In this study, we have also elaborated the most current developments as well as the sensing performance of various SilNW-based Covid-19 sensors. [ABSTRACT FROM AUTHOR]