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Aims & Objective: The fast depletion of fossil fuels and the growing awareness of environmental protection has become a concerning topic. Because of this fact, the researchers are working for a long time to generate electrical energy sources due to the intermittent nature of unconventional energy sources such as solar, wind geothermal, tidal, and biomass as a sustainable, cost-effective, and environmentally friendly alternative for conventional energy sources. These systems are interconnected and full-fill demands as well as energy storage, which subsequently formed a complex hybrid renewable energy system. Hence, forecasting of energy generation, sizing of equipment is essential for the economic feasibility of a complex hybrid system. Also necessary for the design analysis. Methodology: In this research article, the proposed Functional Link Convolutional Neural Network (FLCNN) is applied to forecast the energy generation from the hybrid solar and wind energy system. Also, the Jaya algorithm has been applied to find the optimal sizing of the solar and wind based hybrid renewable energy system. Results & Discussion: The proposed method is simple in design and implementation, and it also reduces computational complexity and time. The proposed FLCNN technique has been compared with various other Machine Learning (ML) methodology, such as Convolutional Neural Network (CNN), Random Forest (RF), and Xg-Boost. In sizing, Jaya is compared with other heuristic techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Cat Swarm Optimization (CSO). Conclusion: The proposed FLCNN and Jaya optimization techniques successfully applied for tasks like energy forecasting and sizing of the renewable energy system.

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Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial neural network (FLANN), is proposed to overcome this problem. Further, the F-score is used to reduce the issue of overfitting by selecting features having a higher significance level. In this paper, FLANN is proposed to classify breast cancer using Wisconsin Breast Cancer Dataset (WBCD) (with 699 samples) and Wisconsin Diagnostic Breast Cancer (WDBC) (with 569 samples) datasets. Experimental results reveal that the proposed models can diagnose breast cancer with higher performance. The proposed model can be used in the early breast cancer diagnosis with 99.41% accuracy.