Lung cancer is the leading cancer among both men and women. A quality preprocessing technique is necessary to ensure effective removal of noise that interferes with the features of the image and hence improves lung cancer detection rate and accuracy. In this proposed research, we will extract basic Geometric Features from Lang DICOM as well as JPEG image Format and Based on Random Forest Classi...

Recently, Wordle has become popular worldwide as a daily puzzle game launched by the New York Times. Players try to solve guessing five-letter word in six tries or less. According Wordle's statistical data, this paper first uses K-means algorithm cluster difficulty of solution words quantify English and analyzes accuracy scientificity clustering results. Then, Random Forest model classify into ...

The widespread use of the Internet has an adverse effect being vulnerable to cyber attacks. Defensive mechanisms like firewalls and IDSs have evolved with a lot research contributions happening in these areas. Machine learning techniques been successfully used defense especially IDSs. Although they are effective some extent identifying new patterns variants existing malicious patterns, many att...

The classification problem is one of the important research subjects in the field of machine learning. However, most machine learning algorithms train a classifier based on the assumption that the number of training examples of classes is almost equal. When a classifier was trained on imbalanced data, the performance of the classifier declined clearly. For resolving the class-imbalanced problem...

Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results...

Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results...

From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on genetic variations at the DNA base pair level, called SingleNucleotide Polymorphisms (SNPs), collected from the Ontario Heart Genomics Study (OHGS). First, the thesis explains two commonly used supervised learning algorithms, the k-Nearest Neighbour (k-NN) and Random Forest classifier...

Improvements in the quality of tourism services and number human resources will affect social information provided to foreign tourists, thereby enhancing offered regarding tourist destination Malang Raya area. Considering urgency tourists obtaining related directions, routes, access roads their desired destinations, especially East Java, due limited data from government agencies handling sector...

Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results...