All over the world, there are a significant number of patients suffering every year from blood cancer. Most of the people are unaware of the risk involved in such a disease. A majority of these diseases are dangerous and may cause death. The patient who have been diagnosed with such a disease, feels very afraid. The patient may feel that the disease is very uncontrolled. Such diseases are very uncommon, and the patient may get very less assistance and information available about this disease. This symptom is called as acute lymphocytic leukemia (ALL) in medical science. In such a kind of cancer, white blood cells are mostly affected. In case of children, this disease is mainly detected i.e. children are more prone to this disease. If the disease is diagnosed in the early stage, the chances of recovery are maximum. Hence, there should be an accurate and guaranteed mechanism available to detect such type of blood cancers in the patients. This work proposes a system to distinguish the three different types of ALL using a convolutional neural network (CNN) by means of microscopic pictures of peripheral blood smears (PBS) and obtain accuracy levels that surpass those of practicing physicians.

The rise of mobile and wireless technology has fundamentally changed how people interact, work together, and compute, giving them quick access to a variety of services that have had a significant impact on their lifestyles. Technology is developing quickly, but this has also raised questions about user privacy, which is important for protecting personal information (PIP). In order to assess the attitudes and understanding of user privacy among 221 Indian internet users, a survey was undertaken. The results are presented in this paper. The results show that users usually have a poor opinion of PIP and have different opinions on issues related to privacy than other people. The paper emphasises the need for a PIP protection system that is user-centric and gives users control over their personal data rather than only depending on service providers. A privacy awareness tool's implementation is also identified as essential for building trust between customers and service providers. The study also examines the motivating factors for these beliefs and investigates potential mitigating strategies for the creation of a strong privacy protection system.

Concerns about online privacy have become more prevalent as a result of the quick spread of internet services. However, customers are frequently presented with lengthy and complicated privacy policy documentation offered by service providers, which prevents them from reading and understanding these policies. In order to overcome this difficulty, this paper suggests a machine learning-based solution that makes it easier to comprehend privacy policies. To develop our method, we sincerely examined the privacy policies of the top 100 Indian-accessible internet services. Whenever a user visits a website, our tool automatically evaluates it and determines if the General Data Protection Regulation (GDPR)'s data protection guidelines are being followed. Users may use these ratings to determine if a certain privacy policy is in compliance with privacy-abiding practices or not. This approach seeks to narrow the knowledge gap between users and privacy policies, promoting informed choice-making and strengthening online privacy protection.