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.

A huge number of three-dimensional models exists on the internet, due to the fact that there are now more three-dimensional modelling and digitizing tools available for ever-increasing applications. The procedures for retrieval of three-dimensional models have thus become even more essential. The subject of this paper is a shape retrieval of 3D models that are signified as triangle meshes. We propose a new method which first computes the descriptor of 3D models through extracting its features, and then divides a model into clusters depending on a descriptor which is invariant to scale and orientation. A Fuzzy C-means clustering method is utilized for dividing the model into clusters. The superior performance and benefits of our method are shown in the results.

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.

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A quick growth of internet technology makes it easy to assemble a huge volume of data as text document; e. g., journals, blogs, network pages, articles, email letters. In text mining application, increasing text space of datasets represent excessive task which makes it hard to pre-processing documents in efficient way to prepare it for text mining application like document clustering. The proposed system focuses on pre-processing document and reduction document space technique to prepare it for clustering technique. The mutual method for text mining problematic is vector space model (VSM), each term represent a features. Thus the proposed system create vector-space mod-el by using pre-processing method to reduce of trivial data from dataset. While the hug dimen-sionality of VSM is resolved by using low-rank SVD. Experiment results show that the proposed system give better document representation results about 10% from previous approach to prepare it for document clustering