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Osteosarcoma is the malignant bone sarcoma that is characterized by widespread genomic disruption and the inclination for metastatic spread. Early detection of osteosarcoma increases the survival rate. Various osteosarcoma detection methods are adopted to detect osteosarcoma at an early stage, but evaluating the slides under the microscope to find the degree of tumor necrosis and tumor result is a major challenge in the medical sector. Hence, an effective detection method is developed using the proposed Fractional-Harris Hawks Optimization-based Generative Adversarial Network (F-HHO-based GAN) for detecting osteosarcoma at an early stage. Here, the proposed F-HHO is designed by the integration of Fractional Calculus and HHO, respectively. Accordingly, the classification of viable tumor, nontumor, and the necrotic tumor is carried out by GAN using the histology image slides. GAN is used to perform osteosarcoma detection based on the features extracted from the image through the process of cell segmentation. The training process of GAN is done using the proposed F-HHO algorithm. However, the proposed F-HHO obtained better performance using the metrics, namely, accuracy, sensitivity, and specificity with the values of 98%, 98%, and 98% for training percentage and 96.282%, 97.552%, and 95.651% for K-fold, respectively.

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Monitoring utility-scale solar arrays was shown to minimize cost of maintenance and help optimize the performance of the array under various conditions. In this paper, we describe the design of an 18 kW experimental facility that consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchanges via the use of wireless data sharing with servers, fusion and control centers, and mobile devices. Research planned at this stage includes developing machine learning methods for fault detection. Preliminary simulation results on fault detection using machine learning are given in this paper.