Skin cancer poses a serious risk to one’s health and can only be effectively treated with early detection. Early identification is critical since skin cancer has a higher fatality rate, and it expands gradually to different areas of the body. The rapid growth of automated diagnosis frameworks has led to the combination of diverse machine learning, deep learning, and computer vision algorithms for detecting clinical samples and atypical skin lesion specimens. Automated methods for recognizing skin cancer that use deep learning techniques are discussed in this article: convolutional neural networks, and, in general, artificial neural networks. The recognition of symmetries is a key point in dealing with the skin cancer image datasets; hence, in developing the appropriate architecture of neural networks, as it can improve the performance and release capacities of the network. The current study emphasizes the need for an automated method to identify skin lesions to reduce the amount of time and effort required for the diagnostic process, as well as the novel aspect of using algorithms based on deep learning for skin lesion detection. The analysis concludes with underlying research directions for the future, which will assist in better addressing the difficulties encountered in human skin cancer recognition. By highlighting the drawbacks and advantages of prior techniques, the authors hope to establish a standard for future analysis in the domain of human skin lesion diagnostics.

In this paper, the fundamentals of a mesh-free particle numerical method for electromagnetic transient simulation are presented. The smoothed particle interpolation methodology is used by considering the particles as interpolation points in which the electromagnetic field components are computed. The particles can be arbitrarily placed in the problem domain: No regular grid, nor connectivity laws among the particles, have to be initially stated. Thus, the particles can be thickened only in distinct confined areas, where the electromagnetic field rapidly varies or in those regions in which objects of complex shape have to be simulated. Maxwell's equations with the assigned boundary and initial conditions in time domain are numerically solved by means of the proposed method. Validation of the model is carried out by comparing the results with those obtained by the FDTD method for a one-dimensional (1-D) case study in order to easily show the capability of the proposed scheme

This study introduces deep convolutional neural network-based methods for the detection and classification of skin lesions, enhancing system accuracy through a combination of architectures, pre-processing techniques and data augmentation. Multiple networks, including XceptionNet, DenseNet, MobileNet, NASNet Mobile, and EfficientNet, were evaluated to test deep learning’s potential in complex, multi-class classification tasks. Training these models on pre-processed datasets with optimized hyper-parameters (e.g., batch size, learning rate, and dropout) improved classification precision for early-stage skin cancers. Evaluation measures such as accuracy and loss confirmed high classification efficiency with minimal overfitting, as the validation results aligned closely with training. DenseNet-201 and MobileNet-V3 Large demonstrated strong generalization abilities, whereas EfficientNetV2-B3 and NASNet Mobile achieved the best balance between accuracy and efficiency. The application of different augmentation rates per class also enhanced the handling of imbalanced data, resulting in more accurate large-scale detection. Comprehensive pre-processing ensured balanced class representation, and EfficientNetV2 models achieved exceptional classification accuracy, attributed to their optimized architecture balancing depth, width, and resolution. These models showed high convergence rates and generalization, supporting their suitability for medical imaging tasks using transfer learning.

This paper investigates the categorization of mammogram images into benign, malignant and normal categories, providing novel approaches based on Deep Convolutional Neural Networks to the early identification and classification of breast lesions. Multiple DCNN models were tested to see how well deep learning worked for difficult, multi-class categorization problems. These models were trained on pre-processed datasets with optimized hyperparameters (e.g., the batch size, learning rate, and dropout) which increased the precision of classification. Evaluation measures like confusion matrices, accuracy, and loss demonstrated their great classification efficiency with low overfitting and the validation results well aligned with the training. DenseNet-201 and MobileNet-V3 Large displayed significant generalization skills, whilst EfficientNetV2-B3 and NASNet Mobile struck the optimum mix of accuracy and efficiency, making them suitable for practical applications. The use of data augmentation also improved the management of data imbalances, resulting in more accurate large-scale detection. Unlike prior approaches, the combination of the architectures, pre-processing approaches, and data augmentation improved the system’s accuracy, indicating that these models are suitable for medical imaging tasks that require transfer learning. The results have shown precise and accurate classifications in terms of dealing with class imbalances and dataset poor quality. In particular, we have not defined a new framework for computer-aided diagnosis here, but we have reviewed a variety of promising solutions for future developments in this field.

A mesh-free particle method is presented for electromagnetic (EM) transient simulation. The basic idea is to obtain numerical solutions for the partial differential equations describing the EM problem in time domain, by using a set of particles, considered as spatial interpolation points of the field variables, arbitrarily placed in the problem domain and by avoiding the use of a regular mesh. Irregular problem geometry with diffused nonhomogeneous media can be modeled only with an initial set of arbitrarily distributed particles. Consistency recovery strategy of the numerical scheme is discussed and enforced. The time dependence is accounted for with an explicit finite-difference scheme. Comparison with analytical solution for a 2-D case study is shown

We consider a Dirichlet problem for a system of equations involving Kirchhoff type $ p_i $-Laplacian differential operators and exhibiting gradient dependence in the reaction term (convection). Using the subsolution-supersolution method, we establish the existence of weak solutions into suitable ordered intervals. Following unified approach, we also provide a comparison argument to obtain positive solutions of certain models.