CBIR (Content Based Image Retrieval) has become a critical domain in the previous decade, owing to the rising demand for image retrieval from multimedia databases. Typically, we take low-level (colour, texture and shape) or high-level (when machine learning techniques are used) features out of the photos. In our research, we examine the CBIR system utilising three machine learning methods, namely SVM (Support Vector Machine), KNN (K Nearest Neighbours), and CNN (Convolution Neural Networks), using Corel 1K, 5K, and 10K databases, by splitting the data into 80% train data and 20% test data. Moreover, compare each algorithm’s accuracy and efficiency when a specific task of image retrieval is given to it. The final outcome of this project will provide us with a clear vision of how effective deep learning, KNN and CNN algorithms are to finish the task of image retrieval.

A major portion of the global energy demand was likely to be fulfilled by an extensive supply of renewable power. Renewable energy outputs, on the other hand, are changeable due to the dynamic nature of their sources. The integration of these variable sources of power into current power grids is proving difficult for electrical power system operators all around the world. The fundamental issue with renewable energy systems is that, due to the stochastic nature of renewable power, electricity production varies from period to period. Recent research and development on renewable technologies can ensure the islands’ long-term electricity supply. Renewable energy sources, on the other hand, are limited by their unpredictable nature and significant reliance on weather conditions. To offset this disadvantage, several renewable energy sources and converters must be joined. To balance the power generation and load power, a hybrid renewable power generation for standalone application is proposed. The solar plant model is made up of a 170 W photovoltaic (PV) panel connected in series, and conversion of energy is done using the maximum power point tracking (MPPT) algorithm, which regulates a buck-boost converter modulation. The MPPT method used in the converter’s control step is based on perturb and observe (P&O) and enhanced with a PI controller. The bidirectional buck-boost DC-DC converters (BBDC) are utilized to preserve a DC-link voltage stable. This is also storing additional hybrid energy in a large battery and is distributed to the system load; then there is a shortage of hybrid power. The load current power is regulated in terms of the frequency and enables it to be achieved using three vector control technique voltage source inverters (VSI). The results were offered to demonstrate a hybrid performance of this organization.