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According to the research and study, cervical cancer has risen to develop the fourth most communal malignancy to strike women. Five different forms of gynaecologic cancer affect the feminine generative organism. The cervix, the lower portion of the body that joins the vagina and the uterus, is where cervical cancer develops in a woman. Cancers, in general, are abnormal alterations in cell development that take place within the human body. Additionally, aberrant cell alterations in the uterine lining or at the womb's opening have been linked to cervical cancer. Additionally, the Artificial Bee Colony (ABC) approach's enhancement of the topography selection process is taken into consideration. This work suggests a novel approach for better identifying the risk factors for cervical cancer in females by combining an evolutionary technique for topography selection with a deep learning model. The lack of specificity regarding the timeframe or demographic affected might limit the study's applicability and generalizability. To create an improvised topography selection, a deep learning method known as LSTM is paired with an evolutionary computation method known as ABC. The model's accuracy is found to be 98.68% when compared to previously used models like SVM-PCA and SVM-BC. Comparing the implemented model to other models, it provided the highest level of accuracy.

The need for connected devices is increasing rapidly in response to the expanding demand for internet connectivity and related services. Due to the high need for IoT applications, new methods and tools have been developed. RPL is a protocol suite used in IoT networks that facilitates communication and movement between nodes. Commercial implementation of the Internet of Thing (IoT) is hampered by a small number of security problems, despite the fact that there are many benefits to adopting IoT. The EVV approach is what the authors suggest utilizing in order to locate the rank node in an RPL topology that has been incorrectly assigned. A rank value is a numerical representation of each node's position in the tree in relation to the root node. To identify the malicious hub, the proposed EVV method is implemented at the root hub. Attackers in RPL use the energy meter to their advantage and launch a variety of attacks by moving up the RPL directed attack graph (DODAG). This work proposes an energy-based intrusion detection module to identify these attacks and the malicious nodes. Against a rank attack, also known as a rank inconsistency attack (RIA), this EVV module can hold its own. Select network parameters are used to evaluate the proposed EVV method against the current systems. Thus, compared to prior methods, the EVV significantly decreased the time required for both attacker identification and network convergence.