Cervical cancer is a disease affecting women worldwide, including in impoverished countries, and it poses a significant threat to their reproductive health. The main drawback of this cancer is that it shows no early signs. Additionally, social factors and individual errors during manual testing hinder screening for this disease. Therefore, developing a model using machine learning and deep learning is a significant and trustworthy option that offers greater support than traditional detection methods. In this research, we use two powerful algorithms, Categorical Boosting (CatBoost) and Convolutional Neural Network (CNN) to tackle the problem of cervical cancer classification. Two datasets are used, Cervical Cancer Risk Factors (CCRF) and Malhari, where the first dataset is textual data. After applying the CatBoost algorithm, we reach an accuracy of 100% for CCRF dataset. The second dataset has two types of images (Pap Smear and Colposcopy), where we reach an accuracy of 87.2% for the Pap Smear type and 91.1% for the Colposcopy type by applying the newly developed CNN algorithm.

The widespread range of wireless sensor networks (WSNs) has recently increased the possibility of attacks. In the cyber-physical system, WSN is considered a significant component, consisting of several hops that self-organize from moving or stationary sensors. Attackers perform abusive operations to enter, seize, and control the WSN network. In order to stop such attacks on sensor networks in the future, the network traffic data was evaluated, dangerous traffic activity was monitored, and nodes were investigated. In this study, we proposed a new improved bidirectional long-short-term memory (Bi-LSTM) algorithm, which is an enhancement of the LSTM model, to address the issue of intrusion detection in WSN systems, which has four steps. In a preprocessing step, the initial raw data collected from the network can be used to train learning models. Then, the analyzed data was classified according to the types of network traffic, and the attacks were detected in the second step. In the next step, our proposed improved learning models showed results with higher accuracy than traditional detection methods. This study assessed the performance of deep learning algorithms on two different datasets, primarily the 'WSN-DS' and 'KDDCup99' datasets, which were chosen to identify and classify different DoS attacks. The primary common WSN attacks were flooding, scheduling, blackhole, and grayhole, which were included in the data set 'WSN-DS', and (denial-of-service (DoS), user-2-root (U2R), root-2-local (R2L), and Probe) were in the 'KDDCup99' dataset. Our newly improved proposed model based on Bi-LSTM achieved an accuracy of 100% on the 'WSN-DS' dataset and 99.9% on the 'KDDCup99' dataset.