A vital component of patient care is the diagnosis of blood cancer, which necessitates prompt and correct classification for efficient treatment planning. The limitations of subjectivity and different levels of skill in manual classification methods highlight the need for automated systems. This study improves blood cancer cell identification and categorization by utilizing deep learning, a subset of artificial intelligence. Our technique uses bespoke U-Net, MobileNet V2, and VGG-16, powerful neural networks to address problems with manual classification. For the purposes biomedical image segmentation U-Net architecture is used, MobileNet V2 is used for lightweight neural network model design and VGG-16 is used for image classification. A hand-picked dataset from Taleqani Hospital in Iran is used for the rigorous training, validation, and testing of the suggested models. The dataset is refined using denoising, augmentation, and linear normalisation, which improves model adaptability. The results show that the MobileNet V2 model outperforms related studies in terms of accuracy (97.42%) when it comes to identifying and categorizing blast cells from acute lymphoblastic leukemia. This work offers a fresh approach that adds to artificial intelligence's potentially revolutionary potential in medical diagnosis.

The popularity of mobile games has expanded among individuals of all ages, and the mobile gaming businesses are quickly expanding day by day. The Google Play Store, one of the most well-known platforms for the distribution of Android applications and games, sees a daily influx of thousands of new mobile games. One of the biggest problems in the gaming industry is predicting a mobile game's performance. Every day, thousands of new games are released. But just a couple of them are successful, while most of them fail. The study was done with the intention of analyzing any relationship between a mobile game's success and its distinctive features. Many of the mobile game developers work independently or work in the mobile game industries to make their games successful on the digital market. Before they are released, game makers can increase the quality of their games if they are confident in their products' commercial viability. For that reason, more than 17,000 games were taken into consideration. We show that the success of a mobile game is clearly influenced by its category, number of supported languages, developer profile, and release month. Furthermore, we show that specific aesthetic features of game symbols are more frequently linked to higher rating counts. We analyzed Google Play Store mobile games data and used a variety of machine learning algorithms for predicting the performance of mobile games based on the total number of downloads and the total user rating.