European Journal of Molecular & Clinical Medicine

Monitoring the traffic of the network for social media servers has become important task to avoid malicious traffic. DDOS attacks can crash the server by denying the service with malicious traffic. Various Machine Learning (ML) models has developed to identify the real traffic and malicious traffic based on network parameters. The aim of the research work is to study the performance of the various machine learning models on DDOS datasets. The feature selection methods are evaluated with ML models. The study concludes the high performance has achieved by using PCA feature selection technique on DDOS classification datasets.

Because nurse care activity identification has a high class imbalance issue and intra-class variability depending on both the subject and the receiver, it is a novel and demanding study topic in human activity recognition (HAR). To address the issue of class imbalance in the Heiseikai data, nurse care activity dataset, we used the Random Forest-based resampling approach. A Gini impurity-based feature selection, model training, and validation using Stratified KFold cross-validation are all part of this technique. Random Forest classification yielded 65.9 percent average cross-validation accuracy in categorising 12 tasks performed by nurses in both laboratory and real-world contexts.. This algorithmic pipeline was created by the "Britter Baire" team for the "2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data."

The main reason of increasing mortality rate among women is the breast cancer. It makes several hours with the less availability of systems to identify the diagnosis of cancer manually. Hence there is a need to develop an automatic system for early detection of cancer. Several researchers have focused in order to improve performance and achieved to obtain satisfactory results. But unfortunately it will be very difficult to detect the cancer in beginning stages because the symptoms may be inappropriate.Therefore, there is a need to determine and acquire a new knowledge to prevent and minimizing the risk of getting effected with cancer. Machine learning (ML) is algorithms are widely used in detecting breast cancer patterns and predict the grading level. Machine learning techniques can be used to classify the stage of cancer, where machine can be trained from past data and build a model so that it can predict the category of new input.In this paper we used K-nearest neighbors (K-NN) and Support Vector Machine (SVM) on the dataset collected from UCI repository to detect breast cancerwith respect to the results of accuracy the efficiency of algorithm is also measured and compared.

Electronic restorative imaging and examination techniques utilizing different modalities have
encouraged early determination. The development of the computer-aided retrivel systems in
recent years turned them into a nondestructive and popular method for diagnosis the disease
in medical images. In this work, adaptive Gabor wavelet filter bank and Texton based a
feature descriptor is developed for medical image retrieval. The design of the proposed
descriptor basis provides flexibility in order to extract the dominant directional features from
medical images.. Also, we present a novel end-to-end integrated deep learning model using
Convolutional Neural Network (CNN) and the Long Short-Term Memory cell (LSTM). The
proposed integrate deep learning descriptor is compared to other descriptor such as CCM,
CHD, MTH and MSD using the datasets such as New Caltech , Corel-1000,Oliva and Corel-
10,000.