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Along with the high spectral rich information it provides, one of the difficulties in processing a hyperspectral image is the need for expert knowledge and high-spec hardware to process very high-dimensional data. 3D convolutional neural network (3D CNN), which uses spectral and spatial features together, enables a powerful solution for HSI classification. This study proposes an efficient dual-stream 3D CNN for accurate HSI classification. The proposed method offers effective classification using spectral-spatial features without relying on pre-processing or post-processing. A comparative study of how CNN classification performance is affected by hyperspectral band selection based on deep reinforcement learning (DRL) is presented. Using the most relevant bands in the hyperspectral image is decisive in deep CNN networks without losing information and accuracy. The proposed method was compared with 3D CNN, 3D + 1D CNN, Multiscale 3D deep convolutional neural network (M3D-DCNN), and InceptionV3 algorithms using Indian Pines (IP), Salinas, Pavia Center (PaviaC), Houston 2013 and QUH-Tangdaowan datasets. It achieved 92.43 % overall accuracy (OA) in IP, 95.06 % OA in Salinas dataset, 99.00 % OA in PaviaC dataset, 91.25 % OA in Houston 2013 and 94.87 % OA in QUH-Tangdaowan. Codes are released at: https://github.com/lapistlazuli/DS-3DCNN. [ABSTRACT FROM AUTHOR]

The operation of the heating, ventilation, and air conditioning (HVAC) system is essential for the indoor thermal environment and is significant for energy consumers in commercial properties. Although earlier studies suggested that reinforcement learning controls could increase HVAC energy savings, they lacked sufficient details regarding end-to-end management. Recently, the focus on gathering and analyzing data from smart meters and buildings connected to energy-saving studies has increased. Deep reinforcement learning (DRL) suggests novel methods for operating HVAC systems and lowering energy usage. This paper evaluates energy consumption by Convolution Recurrent Neural Networks (CRNN), and Deep Reinforcement Learning is used. This is intended to forecast energy use under various climatic circumstances, and the processes are assessed under different communication protocols. The suggested control technique might directly accept quantitative elements, such as climate and indoor air quality conditions, as input and control indoor thermal set - points at a supervisory level by utilizing the deep neural network. In a highly effective office area in the Houston area, time series data, CRNN, and DRL are effectively used to uncover new energy-saving options (TX, USA). The article presents 1-year information from the Net Zero, Energy Star, and Leadership in Energy and Environment Design (LEED)-certified building, demonstrating a potential energy savings of 8% with the presented design. The findings demonstrate how useful the suggested strategy is in assisting building owners in locating new potential for energy conservation. • Deep reinforcement learning (DRL) is used to suggest novel methods for operating HVAC systems and lowering energy usage. • The suggested control technique might directly accept quantitative elements, such as climate and indoor air quality conditions, as input and control indoor thermal set. • The article presents 1-year information from the Net Zero, Energy Star, and Leadership in Energy and Environment Design (LEED)-certified building. [ABSTRACT FROM AUTHOR]