Cervical cancer is a common cancer that affects women all over the world. This is the fourth leading cause of death among women and has no symptoms in its early stages. At the cervix, cervical cancer cells develop slowly. If it can be detected early, this cancer can be successfully treated. Health professionals are now facing a major challenge in detecting such cancer until it spreads rapidly. This study applied various machine learning classification methods to predict cervical cancer using risk factors. The main aim of this research work is to be described of the performance variation of eight most classifications algorithm to detect cervical cancer disease based on the selection of various top features sets from the dataset. Multilayer Perceptron (MLP), Random Forest and k-Nearest Neighbor, Decision Tree, Logistic Regression, SVC, Gradient Boosting, AdaBoost are examples of machine learning classification algorithms that have been used to predict cervical cancer and help in early diagnosis. A variety of approaches are used to avoid missing values in the dataset. To choose the various best features, a combination of feature selection techniques such as Chi-square, SelectBest and Random Forest was used. The performance of those classifications is evaluated using the accuracy, recall, precision and f1-score parameters. On a variety of top feature sets, MLP outperformed other classification models. The majority of classification models, on the other hand, claim to have the highest accuracy on the top 25 features in dataset splitting ratio (70:30). For each model, the percentage of correctly classified instances has been presented and all of the results are then discussed. Medical professionals will be able to use the suggested approach to perform research on cervical cancer.

This study presents the polar polarization mode and average radical intensity flux distribution measurements based on all optical spatial communication systems. The numerical technique that is applied with the simulation model is the diffraction fast Fourier integral transform. Spatial optical sources are used such as continuous wave (CW) laser and vertical cavity surface-emitting laser (VCSEL) with spatial connector and spatial aperture. Spatial temporal polarization effects are taken into account. Percentage encircled flux/average flux radical intensity and polar radical polarization are measured with spatial connector distance and spatial aperture width. The encircled flux is constant at 95 % of its value in the case of VCSEL laser source at a radical distance of exactly 10 μm. However, the encircled flux is constant at 92 %of its value in the case of CW laser source at a radical distance of exactly 6 μm. It is noticed that the encircled flux increases with increasing radial distance of spatial apertures that are used with two suggested light sources. The encircled flux is constant at 93 % of its value in the case of VCSEL laser source at a radical distance of exactly 10 μm.

This work outlines technical specifications of the undersea fiber optic communication channel bandwidth, capacity with taken into account the maximum and minimum extended fiber cost in the presence of amplifiers stations. The number of amplifiers in the amplification stage are addressed based on the amplifier distance to strength the light signal in water depth after 5 km distance. The fiber channel capacity is estimated at different water depth and at the surface of the water. Minimum input signal power and required detectable received power are adjusted to ensure the high data rates in submarine cable systems under the best and worst conditions of the seawater pressure. The study emphasizes the high data rates transmission can be achieved at a distance of 10 km depth.

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