The Problem of Photovoltaic (PV) defects detection and classification has been well studied. Several techniques exist in identifying the defects and localizing them in PV panels that use various features, but suffer to achieve higher performance. An efficient Real-Time Multi Variant Deep learning Model (RMVDM) is presented in this article to handle this issue. The method considers different defects like a spotlight, crack, dust, and micro-cracks to detect the defects as well as localizes the defects. The image data set given has been preprocessed by applying the Region-Based Histogram Approximation (RHA) algorithm. The preprocessed images are applied with Gray Scale Quantization Algorithm (GSQA) to extract the features. Extracted features are trained with a Multi Variant Deep learning model where the model trained with a number of layers belongs to different classes of neurons. Each class neuron has been designed to measure Defect Class Support (DCS). At the test phase, the input image has been applied with different operations, and the features extracted passed through the model trained. The output layer returns a number of DCS values using which the method identifies the class of defect and localizes the defect in the image. Further, the method uses the Higher-Order Texture Localization (HOTL) technique in localizing the defect. The proposed model produces efficient results with around 97% in defect detection and localization with higher accuracy and less time complexity.

Photovoltaic (PV) boards are a perfect way to create eco-friendly power from daylight. The defects in the PV panels are caused by various conditions; such defective PV panels need continuous monitoring. The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants. In general, conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation. The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process. To increase the accuracy and to reduce the processing time, a new Convolutional Neural Network (CNN) architecture is required. Hence, in the present work, a new Real-time Multi Variant Deep learning Model (RMVDM) architecture is proposed, and it extracts the image features and classifies the defects in PV panels quickly with high accuracy. The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images. The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person. The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset. The results show that 98% of the accuracy and recall values in the fault detection and classification process.

Diabetic foot ulcers (DFU) and infections are the most common complications of diabetic foot disease. Mortality and financial burdens for both patients and society on the whole are caused by the prevalence of complications. Peripheral neuropathy, peripheral arterial disease, and immune response dysfunction are just a few of the main contributing factors that must be understood in order to effectively treat the condition. In order to treat diabetic foot disease, you must first get a comprehensive physical examination and a detailed history of your condition. Diabetic neuropathy and peripheral vascular disease, as well as any evidence of diabetic foot ulcers or infection, should be examined during this procedure. Patients with diabetes mellitus were studied to see if there was a link between cognitive impairment and the condition of their feet and whether or not they followed their doctor's recommendations for glycemic control. Using a random sample of diabetes patients, researchers conducted a prospective study to see how many people had the condition. The Mini-Mental State Valuation, the Trail Making Judgments, and the Michigan Screening Instrument were used to assess cognitive abilities. In the one-month follow-up, glycated hemoglobin (HB1Ac >7 %) was linked to the MMSE and medication adherence, but no link was seen between cognitive function and neuropathy. According to the results of a ROC curve investigation, HB1Ac and the MNSI score both significantly (p< 0,05) mitigate towards eventual adherence to medicine for foot problems. For the purpose of determining if DFU was associated with cognitive impairment, the Chi square valuation was used in the statistical examination. As a determinant of MMSE and MoCA scores, the researchers used linear regression to come to their conclusion. Diabetic foot issues should be managed with good blood sugar control and less acute neuropathy, and this does not seem to be linked to cognitive dysfunction. More study is required in order to personalize treatments for diseases of the central and peripheral nervous systems appropriately. Cognitive dysfunction should be taken into account by doctors and podiatrists while treating diabetic foot problems