With the rapid increase in number of cyber-attacks, detecting and preventing malicious behavior has become more important than ever before. In this study, we propose a method for classifying host process data using machine learning algorithms. One challenges study is dealing with high-dimensional imbalanced data. To address this, first preprocessed Principal Component Analysis (PCA) Uniform Man...

İnternet kullanımının yaygınlaşması ve ağa bağlı cihaz sayısının artması ile siber saldırılarla karşılaşma olasılığı artmaktadır. Siber saldırıların verdiği zararları, engellemek için saldırı tespit sistemleri kullanılmaktadır. Bu çalışmada engellenmesi için, evrişimli sinir ağı kullanılarak özellik seçimine dayalı uygulaması gerçekleştirilmiştir. Eğitim test işlemlerinde CSE-CIC-IDS2018 veri s...

This thesis reports the investigations into the task of phone-level pronunciation error detection, the performance of which is heavily affected by the imbalanced distribution of the classes in a manually annotated data set of non-native English (Read Aloud responses from the TOEFL Junior Pilot assessment). In order to address problems caused by this extreme class imbalance, two machine learning...

This article offers policymakers and researchers pragmatic sustainable approaches to identify mitigate conflict threats by looking beyond p-values plausible instruments. We argue that predicting successfully depends on the choice of algorithms, which, if chosen accurately, can reduce economic social instabilities caused post-conflict reconstruction. After collating data with variables linked co...

This study aimed to develop a risk-prediction model for second primary skin cancer (SPSC) survivors. We identified the clinical characteristics of SPSC and created awareness physicians screening high-risk patients among Using data from 1248 survivors extracted five registries, we benchmarked random forest algorithm against MLP, C4.5, AdaBoost, bagging algorithms several metrics. Additionally, i...

Abstract Training a machine learning algorithm on class-imbalanced dataset can be difficult task, process that could prove even more challenging under conditions of high dimensionality. Feature extraction and data sampling are among the most popular preprocessing techniques. is used to derive richer set reduced features, while mitigate class imbalance. In this paper, we investigate these two te...

Accuracies of survival models for life expectancy prediction as well as lifesaving criticalcare applications are significantly compromised due to the sparsity of samples and extreme imbalance between the survival and mortality classes in addition to the invalidity of the popular proportional hazard assumption. An imbalance in data results in an underestimation (overestimation) of the hazard of ...

Moldy apple core is an internal fruit disease that poses a threat to consumer health. In this study, synthetic minority over-sampling technology (SMOTE) based model of moldy was proposed solve the problem poor performance due imbalanced sample distribution in spectral nondestructive detection core. Two different methods, random under-sampling (RUS) and SMOTE, were used balance original samples....

The analysis of geomagnetic suitability is the basis and premise matching navigation positioning. A evaluation model using mixed sampling an improved back propagation neural network (BPNN) based on gray wolf optimization (GWO) algorithm by incorporating dimension learning-based hunting (DLH) search strategy was proposed in this paper to accurately assess suitability. Compared with traditional m...