Cancer is a devastating disease that has far-reaching effects on our culture and economy, in addition to the human lives it takes. Regarding budgetary responsibility, investing just in cancer treatment is not an option. Early diagnosis is a crucial part of the remedy that sometimes gets overlooked. Malignancy is often diagnosed and evaluated using Histopathology Images (HI), which are widely accepted as the gold standard in the field. Yet, even for experienced pathologists, analysing such images is challenging, which raises concerns of inter- and intra-observer variability. The analysis also requires a substantial investment of time and energy. One way that such an examination may be sped up is by making use of computer-assisted diagnostics devices. The purpose of this research is to create a comprehensive cancer detection system using images of breast and prostate histopathology stained with haematoxylin and eosin (H&E). Proposed here is work on improving colour normalisation methods, constructing an integrated model for nuclei segmentation and multiple objects overlap resolution, introducing and evaluating multi-level features for extracting relevant histopathological image and interpretable information, and developing classification algorithms for tasks such as cancer diagnosis, tumor identification, and tumor class labelling. Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network which obtained statistical kappa value for breast cancer histopathology images shows a high degree of consistency in the classification task, with a range of 0.610.80 for benign and low grades and a range of 0.811.0 for medium and high rates. The Support Vector Machine (SVM), on the other hand, shows an almost perfect degree of consistency (0.811.0) across the several breast cancer picture classifications (benign, low, medium, and high).

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Background: Restoration of noisy images from the salt and pepper noise is an interesting area in the field of image processing. The restoration process can be done using various filtering algorithms. The restoration process should not affect the pixels of the original image. The problem of the existing work persists as the increase in the error rate while the dimensions as well as the image format changes. The proposed work consists of Hybrid Adaptive Switching median filtering (HASMF). The hybrid technique corrupted images’ high-density salt and pepper noise removal using Ant colony Optimization technique. This hybrid technique would remove the high-density salt and pepper noise from the corrupted images. The noisy pixel value from the corrupted images is identified and selected using the Ant Colony Optimization technique (ACO). The identified corrupted value can be replaced using the Adaptive Switching Median Filter. The switching process is carried out using the pixel by pixel with the normalized median values. The noisy pixels are identified and selected using Ant colony Optimization. The optimized values are subjected to the filtering process. The proposed method decreases the salt and pepper noise within the original image. The hybrid design approach was used in the proposed study, which used 45 nm technology combined with a Verilog-A model-based circuit that was implemented using Spintronic. It was discovered that the suggested changed task had less latency, used less space, and dissipated less power than the original. Furthermore, it was discovered that designed memory arrays were both energy and space-efficient. It does not affect the normal pixels within the original image. The comparison process has been made with the various existing algorithms such as Median Filter (MF), Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). The proposed method has overcome the various performance metrics such as Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index (SSIM). The results obtained have shown the significant results in terms of object measures as well as visual perception of the denoised image.

Deep learning based novel intelligent neural network models are developed in this research study and employed for performing multi‐step wind speed and wind power forecasting for the data pertaining to certain wind farms. It has always been tedious to predict wind speed and wind power accurately due the existence of non‐linearity in the wind farm data and as well previous traditional and heuristic techniques has their own merits and demerits in performing the prediction process. This research study intends to handle the prevailing non‐linearity of the wind farm data and as well perform prediction of the parameters in a better manner with increased accuracy rate. The prediction study facilitates the renewable energy community to install the wind mills in the locations with higher accuracy rate and thereby power production gets increased extravagantly. The intelligent neural network developed in this article includes the ELMAN and spiking neuronal models with incorporated deep learning procedure and varying momentum factor criterion to achieve minimal error and better accuracy rate. Multi‐step forecasting is carried for 10‐min ahead and the numerical simulation executed with the proposed intelligent non‐linear forecasting techniques. The attained results confirm the superiority of the developed models over other techniques from previous works.