One of the most common types of cancer in women is cervical cancer, a disease which is the most prevalent in poor nations, with one woman dying from it every two minutes. It has a major impact on the cancer burden in all cultures and economies. Clinicians have planned to use improvements in digital imaging and machine learning to enhance cervical cancer screening in recent years. Even while most cervical infections, which generate positive tests, do not result in precancer, women who test negative are at low risk for cervical cancer over the next decade. The problem is determining which women with positive HPV test results are more likely to have precancerous alterations in their cervical cells and, as a result, should have a colposcopy to inspect the cervix and collect samples for biopsy, or who requires urgent treatment. Previous research has suggested techniques to automate the dual-stain assessment, which has significant clinical implications. The authors reviewed previous research and proposed the cancer risk prediction model using deep learning. This model initially imports dataset and libraries for data analysis and posts which data standardization and basic visualization was performed. Finally, the model was designed and trained to predict cervical cancer, and the accuracy and performance were evaluated using the Cervical Cancer dataset.

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in recent years, a lot of research is focused on wireless sensor network applications, which is focused on field of performance, security, and energy. This paper addressed the difficulties and challenges facing the wireless sensor networks on the battlefield. Which is often vulnerable to attacker's networks either in the data or corrupting control devices and attempt to consume a lot of energy by sending a large quantity of useless packets, which contributes to excessive consumption of energy and leads to exit nodes from work. Since technology has become widespread on battlefields at the present time, then the sensor nodes are vulnerable to attackers from both sides. This research discussed many challenges and gave appropriate solutions. The simulations showed that these solutions can help secure data and saved 40% of energy consumed.

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