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Aiming at the low efficiency of antenna modeling and solving the problems of slow training speed and high resource consumption when multiple antenna structural parameters are optimized simultaneously, a new deep multi-layer convolutional neural network (DMCNN) is proposed. The DMCNN model uses four layers of ladder-type fully connected layers combined with convolutional layers to improve the learning and classification capabilities of data features. It adds a LeakyReLU activation function to solve the problem of neurons disappearing in negative value areas and alternately uses maximum pooling and average pooling operations to update training parameters to reduce the amount of calculation and maintain feature invariance. Apply Adam optimizer combined with dropout technology to adjust the learning rate and reduce weight fluctuations. The DMCNN model extracts samples from the geometric construction parameters of the ultra-wideband multiple-input multiple-output (UWB MIMO) antenna as feature input to predict the return loss $S_{11}$ and insertion loss $S_{21}$. Simulation results prove that the DMCNN model’s prediction fit for $S_{11}$ and $S_{21}$ reached 98.48% and 95.02%, respectively. Compared with the multi-layer perceptron (MLP) and Fully Connected Neural Network (FCNN) models, the training effect was improved by at least 11.66%. It solves the shortcomings of traditional methods, and has excellent UWB MIMO antenna modeling capabilities.

Skin cancer is one of the most dangerous cancers that may occur for different age groups of people. As a result, early identification of skin cancer has the potential to save millions of lives. In Traditional machine learning approaches, there are various drawbacks in detection and classification of skin lesions. As a result, to achieve the robust performance, initially the joint trilateral and bilateral filter (JTBF) with convolutional auto encoder and decoder (CAED)-based preprocessing method is used to enhance the skin lesion and also removes hair from lesions. Then, transfer learning-based probabilistic multi-layer dense networks (PMDN) method-based unmanned Transfer learning segmentation method is adapted for accurately detecting the cancer region on skin lesions. Further, transfer learning convolution neural network (TL-CNN) is used to extract the features from the segmented region, which extracts the detailed inter-disease-dependent (IDD) and intra-disease specific (IDS) features. Finally, Alexa Net model is trained and tested with the IDD, IDS features and classifies the eight different skin cancer types. The complexity of the transfer learning networks is optimized by the using the Adam optimizer. Finally, the simulation results show that the proposed model resulted in superior segmentation, feature extraction, and classification performances as compared to conventional approaches. Further, the proposed method achieved 99.937% segmentation accuracy, 99.47% feature extraction accuracy, and 99.27% classification accuracy on ISIC-2019 public challenge dataset.

Medical image analysis gains huge advancements with deep learning approaches which boosts the computational capability and accurate prediction. An expert in the field analyzes the feature representation, and an intelligent model helps to categorize and forecast diseases. The MRI images from cancer imaging archives are used to give a novel learning approach for soft tissue diagnosis. This work suggests a Type-2 fuzzy model that chooses local and maximum absolute value to handle the data. According to the experimental investigation, the predicted model works better than several current strategies. Deep convolutional neural networks built on the VGG-16 architecture and the Adam optimizers are used to pre-train the proposed model. The classification accuracy is anticipated based on the experimentation, demonstrating the need for complementary information for learning systems. The goal is to forecast the depth of knowledge extraction by boosting pre-trained CNN’s ability to be fine-tuned by improving the accuracy of soft tissue classification. The proposed model improves performance and validates the significance of the network model by categorizing the tissues as benign or cancerous. The proposed model attains 96.8% accuracy and 9% for depths of 5 and 8 mm and 93% for 10 mm. Similarly, the model attains 96.8% without inclusion, 100% with inclusion and an average outcome of 99%.