Breast cancer is the second leading type of cancer, which is mainly found in women and which increases the death rate among women. Early detection and diagnosis of breast cancer can reduce its occurrence and the death rate. Unfortunately, even if cancer treatment is initiated quickly after diagnosis, cancer may relapse because cancer cells may continue to exist in the body, which is also a major problem faced by women who fear facing the same treatment twice. So, detecting cancer at its early stage and predicting the recurrence of it is a major issue in the medical field that needs to be solved. Machine learning (ML) algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and voting classifier (VC) are used for breast cancer prediction. Due to high-dimension data, the predicted results using Machine learning algorithms will increase the errors and decrease the accuracy. So, bioinspired algorithms such as the genetic algorithm (GA) and sand cat swarm optimization (SCSO) are used to reduce the data dimension. Convolutional neural network (CNN) is used for feature extraction from the image dataset. CNN algorithms are used for feature selection, which selects the important features for classification and prediction by applying 10 cross-validation methods. The proposed model using bioinspired optimization algorithms outcomes will yield high accuracy and the best solution.

Lockdowns were implemented in nearly all countries in the world in order to reduce the spread of COVID-19. The majority of the production activities like industries, transportation, and construction were restricted completely. This unprecedented stagnation of resident's consumption and industrial production has efficiently reduced air pollution emissions, providing typical and natural test sites to estimate the effects of human activity controlling on air pollution control and reduction. Air pollutants impose higher risks on the health of human beings and also damage the ecosystem. Previous research has used machine learning (ML) and statistical modeling to categorize and predict air pollution. This study developed a binary spring search optimization with hybrid deep learning (BSSO-HDL) for air pollution prediction and an air quality index (AQI) classification process during the pandemic. At the initial stage, the BSSO-HDL model pre-processes the actual air quality data and makes it compatible for further processing. In the presented BSSO-HDL model, an HDL-based air quality prediction and AQI classification model was applied in which the HDL was derived by the use of a convolutional neural network with an extreme learning machine (CNN-ELM) algorithm. To optimally modify the hyperparameter values of the BSSO-HDL model, the BSSO algorithm-based hyperparameter tuning procedure gets executed. The experimental outcome demonstrates the promising prediction classification performance of the BSSO-HDL model. This model, developed on the Python platform, was evaluated using the coefficient of determination R², the mean absolute error (MAE), and the root mean squared error (RMSE) error measures. With an R² of 0.922, RMSE of 15.422, and MAE of 10.029, the suggested BSSO-HDL technique outperforms established models such as XGBoost, support vector machines (SVM), random forest (RF), and the ensemble model (EM). This demonstrates its ability in providing precise and reliable AQI predictions.

With the realm of cutting-edge innovations, technology stands out as a pivotal tool, contributing to both digital expansions and disruptions. Blockchain technology, a decentralized platform for transactions, plays a crucial role in this landscape. All transactions within this framework are meticulously recorded on a distributed public ledger, visible to all participants in the blockchain network. The primary goals of blockchain include providing security, privacy, transparency, and anonymity. As a technological innovation, blockchain has undergone gradual development and is poised to continue transforming the world, emerging as a prominent research topic. Given its rapid growth, a comprehensive evaluation process is essential, attracting interest from researchers, businesses, and clients seeking to address various security challenges through blockchain. This chapter focuses on detailed evolutionary trends of the four blockchain phases. <!----> <!----> <!----> <!---->