Leukemia is one of the deadliest diseases in human life, it is a type of cancer that hits blood cells. The task of diagnosing Leukemia is time consuming and tedious for doctors; it is also challenging to determine the level and type of Leukemia. The diagnoses of Leukemia are achieved through identifying the changes on the White blood Cells (WBC). WBCs are divided into five types: Neutrophils, Eosinophils, Basophils, Monocytes, and Lymphocytes. In this paper, the authors propose a Convolutional Neural Network to detect and classify normal white blood cells. The program will learn about the shape and type of normal WBC by performing the following two tasks. The first task is identifying high level features of a normal white blood cell. The second task is classifying the normal white blood cell according to its type. Using a Convolutional Neural Network CNN, the system will be able to detect normal WBCs by comparing them with the high-level features of normal WBC. This process of identifying and classifying WBC can be vital for doctors and medical staff to make a decision. The proposed network achieves an accuracy up to 96.78% with a dataset including 10,000 blood cell images.

The current period is dominated by social networks in daily life. Despite the several efforts and research practices done to overcome the issue, it still presents a problem. Despite the fact that social networks are useful for social gathering and communication, they also present new opportunities for harmful criminal acts. Cyber-harassment is an example that is enabled through the mistreatment and abuse of the internet as a means of harassing or bullying others virtually. To minimize these occurrences, research into computer-based methods has been per-formed to detect cyber-harassment. This literature survey shows that supervised learning methods were mostly used for cyber-bullying detection. Moreover, some non-supervised methods and other techniques have also shown to be effective in terms of accuracy towards cyber-bullying detection. This paper, therefore, surveys existing recent research on non-supervised techniques as well as it summarizes accuracy results obtained from several papers to discuss the significance of non-supervised learning approaches in comparison with traditional learning methods.

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