Enhanced feature selection algorithm for pneumonia detection

Pneumonia is a type of lung disease that can be detected using X-ray images. The analysis of chest X-ray images is an active research area in medical image analysis and computer-aided radiology. This research aims to improve the accuracy and efficiency of radiologists' work by providing a technique for identifying and categorizing diseases. More attention should be given to applying machine learning approaches to develop a robust chest X-ray image classification method. The typical method for detecting Pneumonia is through chest X-ray images, but analyzing these images can be complex and requires the expertise of a radiographer. This paper demonstrates the feasibility of detecting the disease using chest X-ray images as datasets and a Support Vector Machine combined with a Naive Bayesian classifier, with PCA and GA as feature selection methods. The selected features are essential for training many classifiers. The proposed system achieved an accuracy of 92.26%, using 91% of the principal component. The study's result suggests that using PCA and GA for feature selection in chest X-ray image classification can achieve a good accuracy of 97.44%. Further research is needed to explore the use of other data mining models and care components to improve the accuracy and effectiveness of the system

Proposed neural intrusion detection system to detect denial of service attacks in MANETs

MANTs are groups of mobiles hosts that arrange themselves into a grid lacking some preexist organization where the active network environment makes it simple in danger by an attacker. A node leaves out, and another node enters in the network, making it easy to penetration. This paper aims to design a new method of intrusion detection in the MANET and avoiding Denial of Service (DoS) basis on the neural networks and Zone Sampling-Based Traceback algorithm (ZSBT). There are several restrictions in outdating intrusion detection, such as time-intense, regular informing, non-adaptive, accuracy, and suppleness. Therefore, a novel intrusion detection system is stimulated by Artificial Neural Network and ZSBT algorithm using a simulated MANET. Using KDD cup 99 as a dataset, the experiments demonstrate that the model could can detect DoS effectively