Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques

More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically fatal. Any particular body part may become infected by cancerous cells, which can be fatal. One of the most prevalent types of cancer is skin cancer, which is spreading worldwide.The primary subtypes of skin cancer are squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and accounts for the majority of fatalities. Screening for skin cancer is so crucial.Deep Learning is one of the best options to quickly and precisely diagnose skin cancer (DL).This study used the Convolution Neural Network (CNN) deep learning technique to distinguish between the two primary types of cancers, malignant and benign, using the ISIC2018 dataset. The 3533 skin lesions in this dataset range from benign to malignant, and nonmelanocytic to melanocytic malignancies. The images were initially enhanced and edited using ESRGAN. The preprocessing stage involved resizing, normalising, and augmenting the images. By combining the results of numerous repetitions, the CNN approach might be used to categorise images of skin lesions. Several transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were then used for fine-tuning. The uniqueness and contribution of this study are the preprocessing stages using ESRGAN and the testing of various models (including the intended CNN, Resnet50, InceptionV3, and Inception Resnet). Results from the model we developed matched those from the pretrained model exactly. The efficiency of the suggested strategy was proved by simulations using the ISIC 2018 skin lesion dataset. In terms of accuracy, the CNN model performed better than the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models

Security Enhancement in Surveillance Cloud Using Machine Learning Techniques

Most industries are now switching from traditional modes to cloud environments and cloud-based services. It is essential to create a secure environment for the cloud space in order to provide consumers with a safe and protected environment for cloud-based transactions. Here, we discuss the suggested approaches for creating a reliable and safe environment for a surveillance cloud. When assessing the security of vital locations, surveillance data is crucial. We are implementing machine learning methods to improve cloud security to more precisely classify image pixels, we make use of Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM). We also extend the conventional two-tiered design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.In our work we  evaluates how well our proposed model (FCM-SVM) performed against contemporary models like ANN, KNN, SVD, and Naive Bayes. Comparing our model to other cutting-edge models, we found that it performed better, with an average accuracy of 94.4%

Space charge and conductivity measurement of XLPE nanocomposites for HVDC insulation-permittivity as a nanofiller selection parameter

Cross-linked polyethylene (XLPE) insulation is successfully used for high-voltage AC transmission. However, it is still under development for high-voltage DC application due to space charge accumulation, which distorts the internal electrical field distribution and leads to its aging/failure. Therefore, the space charge should be measured and carefully analysed. On the other side, conductivity measurement helps to forecast the degradation probability of the insulation. Higher conductivity represents the severe degradation. Nanofiller addition, such as SiO2, TiO2, MgO and so on (< 5 wt%), particularly surface-modified nanofiller due to its better dispersion significantly suppresses the space charge accumulation and conductivity. Nevertheless, the choice of suitable nanofiller has still remained a challenge. With this context, space charge and conductivity of XLPE-silica and XLPEmagnesium oxide (MgO) surface-modified nanocomposites are measured. This study proposes a parameter for nanofiller selection that will deliver optimal properties for the intended application. Results show that nanocomposites with higher nanofiller permittivity (i.e. MgO) have less space charge accumulation and low conductivity and are justified with the help of a band gap theory model