Clustering is done in wireless sensor networks (WSN) to conserve the energy of sensor nodes in the network. Decreasing the energy consumption of nodes prolongs the lifetime of the WSN. A quantum bit can exist in “0” state, “1” state or a linear superposition of “0” and “1” states, unlike the binary bit which can exist in only “0” state or “1” state. In this paper, we propose a Quantum inspired PSO (particle swarm optimization) called Quantum-inspired PSO for Energy Efficient Clustering (QPSOEEC). The algorithm is tested by giving different values to the number of sensor nodes and cluster heads, varying the base station position, etc. Then, our results are compared to existing algorithms that demonstrate the superiority of our algorithm.

Data categorization is the technique for classifying and arranging facts based on their value and sensitivity, in addition to offering suitable control and safety precautions. Data categorization is critical in cloud computing for finance because it helps to defend cloud-based monetary data. In this research, we aim to create an Artificial Intelligence (AI)-based totally Data type method for economic cloud computing. We proposed an innovative set of rules named Dynamic Vortex Search-driven Light Gradient Boosting Machine (DVS-LightGBM) for enhancing the method for classifying data in monetary cloud computing. The text cleaning process was conducted to preprocess the collected raw data, and it enhances the quality of the data acquired. We applied Latent Semantic Analysis (LSA) to extract the essential traits from the processed data. The dynamic vortex technique improves accuracy and increases convergence in the data classification process. The data classification procedure is based on cloud sensitivity levels. Our recommended approach consists of three classes, including standard, sensitive and highly sensitive. We implemented our suggested model in Python software. The result analysis phase is done on various parameters such as precision, accuracy, F1 score and recall. We conducted a comparison analysis with other existing methodologies to examine the effectiveness of the suggested model. The results of the trial demonstrate that the recommended strategy outperformed other existing methods in the data classification approach for financial cloud computing.

The Simple Genetic Algorithm evaluates a group of binary strings on the basis of their fitness, performs crossover and mutation on them and tries to generate a group having maximum fitness. The usual method used for implementing the SGA is by using character arrays for storage of binary strings. But, this method has some disadvantages. The SGA implementation can be termed a success if the average fitness of the new generation is more than the initial average fitness. In this paper, we plan to implement the SGA using integer arrays for storage of binary strings. Then, we plan to compare the initial average fitness with the final average fitness so that the working of SGA can be verified. We have written the application such that varying population sizes can be given to check the correctness of the SGA algorithm.