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This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated significant improvements in image analysis and classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise in addressing challenges related to small datasets and limited computational resources. This paper reviews the advantages of CNN and transfer learning in medical imaging, including improved accuracy, reduced time and resource requirements, and the ability to address class imbalances. It also discusses challenges, such as the need for large and diverse datasets, and the limited interpretability of deep learning models. What factors contribute to the success of these networks? How are they fashioned, exactly? What motivated them to build the structures that they did? Finally, the paper presents current and future research directions and opportunities, including the development of specialized architectures and the exploration of new modalities and applications for medical imaging using CNN and transfer learning techniques. Overall, the paper highlights the significant potential of CNN and transfer learning in the field of medical imaging, while also acknowledging the need for continued research and development to overcome existing challenges and limitations.

In this paper, a novel hybrid model for the fault diagnosis of photovoltaic (PV) modules was developed. The model combines a convolutional neural network (CNN) with a machine learning (ML) algorithm. A total of seven defects were considered in this study: sand accumulated on PV modules, covered PV modules, cracked PV modules, degradation, dirty PV modules, short-circuited PV modules, and overheated bypass diodes. First, the hybrid CNN–ML has been developed to classify the seven common defects that occur in PV modules. Second, the developed model has been then optimized. Third, the optimized model has been implemented into a microprocessor (Raspberry Pi 4) for real-time application. Finally, a friendly graphical user interface (GUI) has been designed to help users analyze their PV modules. The proposed hybrid model was extensively evaluated by a comprehensive database collected from three regions with different climatic conditions (Mediterranean, arid, and semi-arid climates). Experimental tests showed the feasibility of such an embedded solution in the diagnosis of PV modules. A comparative study with the state-of-the-art models and our model has been also presented in this paper.

In arid regions, the behavior of solar panels changes significantly compared to the datasheets provided by the manufacturer. Therefore, the objective of this study is to determine the performance of both polycrystalline and monocrystalline solar modules in an arid region characterized by a large potential for solar irradiation and high temperatures. The influence of environmental parameters, such as temperature and dust, on the output power of solar modules with different technologies (monocrystalline and polycrystalline) has been investigated. The Artificial Hummingbirds Algorithm (AHA) has been used to extract parameters for PV modules. As a result, it has been demonstrated that for high solar irradiation, the polycrystalline PV module experiences a smaller decrease in output power than the monocrystalline PV module as the module temperature increases. The percentage drop in output power is approximately 14% for the polycrystalline PV module and nearly 16% for the monocrystalline PV module. However, for low solar irradiation, it is advisable to use monocrystalline modules, as a 21% decrease in power was observed for polycrystalline modules compared to a 9% decrease for monocrystalline modules. Additionally, the monocrystalline PV module was more affected by dust than the polycrystalline PV module under high solar irradiation conditions, while under low incident solar radiation, the polycrystalline PV module was more affected by dust than the monocrystalline PV module. The power drop of the monocrystalline PV module was greater than that of the polycrystalline PV module for high solar radiation (>500 W/m2). Therefore, the advantage of this proposed work is to recommend the use of polycrystalline solar panels in regions characterized by high solar irradiation and high temperatures instead of monocrystalline solar panels, which are more efficient in regions worldwide characterized by low solar irradiation and low temperatures.