A novel artificial intelligence approach to detect the breast cancer using KNNet technique with EPM gene profiling.

Women's most frequent type of cancer is breast cancer, second only to lung cancer. This paper summarizes changes in genomics and epigenetics and incremental biological activities. A tumour develops through a series of phases involving a separate abnormal gene. Even though many diseases cause DNA mutations, most treatments are designed to relieve symptoms rather than change the DNA. Clustering short palindromic repeats (CRISPR) or Cas9 is the primary approach for discovering and confirming tumorigenic genomic targets. A Kohonen neural network with an expression programming model was developed for gene selection. The main problem in genetic selection is reducing the number of features chosen while maintaining accuracy. This purpose is accomplished systematically. In the end, the approach method performed better than the existing quantum squirrel-inspired algorithm and the recurrent neural network oppositional call search algorithm for genetic selection. The KNNet-EPM model used an expression programming approach to identify gene biomarkers for breast cancer. This method was achieved with RAE of 42%, sensitivity of 93%, f1 score of 88%, accuracy of 98%, kappa score of 83%, specificity of 92% and MAE of 30%.

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media.

The emergence of social media has given rise to a variety of networking and communication opportunities, as well as the well-known issue of cyberbullying, which is continuously on the rise in the current world. Researchers have been actively addressing cyberbullying for a long time by applying machine learning and deep learning techniques. However, although these algorithms have performed well on artificial datasets, they do not provide similar results when applied to real-time datasets with high levels of noise and imbalance. Consequently, finding generic algorithms that can work on dynamic data available across several platforms is critical. This study used a unique hybrid random forest-based CNN model for text classification, combining the strengths of both approaches. Real-time datasets from Twitter and Instagram were collected and annotated to demonstrate the effectiveness of the proposed technique. The performance of various ML and DL algorithms was compared, and the RF-based CNN model outperformed them in accuracy and execution speed. This is particularly important for timely detection of bullying episodes and providing assistance to victims. The model achieved an accuracy of 96% and delivered results 3.4 seconds faster than standard CNN models.