No abstract

The heating, ventilation, and air conditioning (HVAC) control system is in charge of the building's energy efficiency. Indoor energy consumption trends can be intelligently monitored and minimized. Occupancy data is essential for saving a significant amount of energy. This energy footprint can play an important part in modern smart buildings to improve indoor green environments while lowering costs. Traditional energy monitoring and control systems can be enhanced by installing an occupancy monitoring system, which consists of a network of sensors and cameras. In this paper, we offer a novel and innovative convolutional neural network (CNN) based on real-time camera occupancy detection and recognition algorithms across various types of sensors, which provides realistic low-cost energy-saving solutions with robust graphical processing units (GPUs). Decision-making tools can be used to select the appropriate occupancy detection and recognition alternative for indoor environment and energy monitoring and management. In this research work, we develop the "Fermatean fuzzy prioritized weighted average (FFPWA) operator and Fermatean fuzzy prioritized weighted geometric (FFPWG) operator". In the end, we give an algorithm for an intelligent decision support system (IDSS) using proposed AOs to compare our CNN based method with other existing sensors techniques.

In the present study, pure, Co, Gd, and Co/Gd di-doped ZnO nanoparticles were synthesized via the co-precipitation synthesis route. The prepared samples were characterized through different techniques such as the X-ray diffraction method (XRD), scanning electron microscopy (SEM), UV-Vis spectroscopy, photoluminescence (PL)spectroscopy, and an impedance analyzer and vibrating sample magnetometer (VSM). The XRD pattern shows ZnO’s wurtzite hexagonal crystal structure; moreover, the shifting of characteristic peaks toward the lower angle indicates the inclusion of Co and Co/Gd in the ZnO host lattice. SEM micrographs show various morphologies such as rods, the agglomeration of particles, and spherical nanoparticles. The UV-Vis spectroscopy reveals that the absorption increased in the visible region and there was a substantial redshift for the doped samples. The bandgap decreased from 3.34 to 3.18 eV for the doped samples. The PL spectra show near-edge and inter-band transitions; the origin of inter-band transitions is attributed to the defect states present within the bands. The dielectric constant is strongly frequency dependent and decreases with Co and Co/Gd doping, while the electrical conductivity increases. A VSM study indicates that pure ZnO is diamagnetic, while the Co and Co/Gd doped ZnO nanoparticles showed ferromagnetic behavior. Under UV-visible light irradiation, the Co/Gd-ZnO nanoparticles showed higher photocatalytic activity than the ZnO nanoparticles. The enhanced photocatalytic activity may be attributed to a decreased bandgap with doping.