Computational time is high for Multilayer perceptron (MLP) trained with back propagation learning algorithm (BP) also the complexity of the network increases with the number of layers and number of nodes in layers. In contrast to MLP, functional link artificial neural network (FLANN) has less architectural complexity, easier to train, and gives better result in the classification problems. The paper proposed an evolutionary functional link artificial neural network (EFLANN) using genetic algorithm (GA) by eliminating features having little or no predictive information. Particle swarm optimization (PSO) is used as learning tool for solving the problem of classification in data mining. EFLANN overcomes the non-linearity nature of problems by using the functionally expanded selected features, which is commonly encountered in single layer neural networks. The model is empirically compared to MLP, FLANN gradient descent learning algorithm, Radial Basis Function (RBF) and Hybrid Functional Link Neural Network (HFLANN) . The results proved that the proposed model outperforms the other models.

Financial cloud computing is based on the virtualization technology paradigm, which has recently gained prominence in the information technology (IT) industry. The resource allocation in the existing system is not guaranteed, and convergence problems occasionally cause the process to move slowly. Consequently, there has been a notable decline in the overall efficacy of financial cloud computing. In this work, new methods are presented to improve the financial cloud. The three main stages of the proposed system are resource allocation, load balancing and cost-effective virtual machine (VM) migration. To enhance load balancing, this study employs the Enhanced Weighted Round Robin Algorithm (EWRR) technique. Load balancing is achieved by shifting workloads from overloaded to underloaded nodes. The Differential Evaluation-based Bat Algorithm (DEBA) efficiently chooses more optimal resources to accomplish the optimal resource allocation in the financial cloud. Resolute Support Vector Machine (RSVM) technique is utilized to Provide Cost-effective VM migration. The simulation's findings show that the recommended DEBA-EWRR algorithm performs better than existing techniques due to advancements in throughput, Makes pan, Fitness score and resource utilization.

The majority of illnesses are caused by alterations in the environment and food, as well as problems with the body's internal processes. For instance, pneumonia presents serious dangers to public health and can cause lung disorders as a result of lung infection. Accurately and promptly diagnosing lung infections is essential to saving lives. In order to address the aforementioned challenges with a variety of performance evaluations, a deep learning model utilizing several approaches (such as CNN, ResNet) is proposed to find out lung infection in human body. Specifically, our proposed model is thought to be evaluated for pneumonia. As a result, several sets of Chest X-ray (CXR) image data for pneumonia are taken into consideration for testing. The aforementioned dataset is used to illustrate the experiment, and the suggested techniques are used to assess it using certain evaluation measures. Comparative performance study shows that our suggested approaches perform noticeably better than current processes. According to the experiment, ResNet did well in terms of validation accuracy (64.69%), whereas CNN did well in terms of training accuracy (86.47%).