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The current project aims to model and compare the performance of fake news detectors using machine learning algorithms to recognize fake news connected to political topics with high accuracy. The Decision Tree algorithm and the Random Forest algorithm are two algorithms. The methods were developed and evaluated on a dataset including 44,000 samples. Implemented each algorithm through programs and performed ten iterations with different scales of false feeds and factual feeds classification were identified. The G-power test is around 80% accurate. For detecting false political news, the Decision Tree algorithm had a mean accuracy of 99.6990, and the Random Forest approach had a mean accuracy of 98.6380, according to the trial results. The significance of accuracy is p=0.001, indicating the efficacy of the classifier. This research aims to use a novel strategy for contemporary Machine Learning Classifiers to predict fake political news. The comparison results reveal that the Decision Tree method outperforms the Random Forest technique.

This work’s main objective is to predict the attacker’s behavior pattern with high accuracy by using machine learning methods. According to the experimental and statical analysis, the proposed model has improved accuracy. The study was performed with two machine learning algorithms, K-Nearest Neighbor (KNN) and Decision Tree (DTA). On a dataset of 19,864 items, the algorithms were implemented, trained, and assessed. Two iterations have extracted the trained and tested dataset on the sample size. Each algorithm has undergone ten iterations with different test sizes to get different result sets. This study’s G-Power test for machine learning algorithms is roughly 80%. The result sets of the programming experiment have been further analyzed with statistical tools and observed that the accuracy of KNN is 99.76, while DTA is 98.84, according to the testing data. By conducting the independent samples t-tests, the statistical difference is p

The primary goal of the current work is to carry out Malware Detection for IoT devices by comparing the performance of different classifiers. Malware is software that causes harm to our systems or network. Random Forest Algorithm (RFA) and Decision Tree Algorithm (DTA) are two types of algorithms that can be considered. The methods were built and evaluated on a 19612 record dataset. With 10 example sizes, emphasis was performed on each gathering to accomplish better precision. The error rate power was utilized as 80% to perform G-power testing. The experiment’s findings revealed that the Random Forest Algorithm had a mean accuracy of 99.0320 and the Decision tree had a mean accuracy of 98.5140 for malware detection. Using independent sample t-tests, the statistically significant variance in accurateness between the two models was obtained as p = 0.030. This research aims to apply a novel technique to present Machine Learning Classifiers for malware detection. When comparing the Random Forest Algorithm to the Decision Tree Algorithm, the findings signify that the RFA outperforms the DTA.