Homicide rates are still high in the world and they are the worst crime in human existence. Despite all the technological advances and usage of information by various agencies, the number of homicides is not decreasing. Homicide prediction in certain countries should notably be the number one priority, which can help the government to easily identify the kind of profile they are looking for, or even help them prevent those cases. This paper compares different Machine Learning Techniques classifications of homicide prediction. Random Forest (RF), Random Tree, J48, Naive Bayes and k-Nearest-Neighbor (KNN) were tested to determine which method provides the best results in homicide prediction classification. The results of sample accuracy for all algorithms were around 99%, which clearly shows that all algorithms give great results. However, J48 is the best technique applied on the dataset, as it classified all instances correctly.

Since the early beginnings of education systems, attendance has always played a crucial role in student success, as well as in the overall interest of the matter. The most productive way of increasing the student attendance rate is to understand why it decreases, try to predict when it is going to happen, and act on causing factors in order to prevent it. Many benefits of predicted and increased attendance rate can be achieved, including better lecture organization (i.e. lecture time and duration, lecture class choice, etc). This paper describes the steps in the extraction of knowledge from the university's student database and making a model that predicts whether the student will attend the class or not. Results show that the attendance patterns are best reflected when employing a decision tree algorithm, a C4.5 model that is interpretable and able to predict the attendance with 0.81 AUC performance measure

It can be confidently stated that access to education is one of the most prized possessions available to us today. Although there are underlying factors such as the discrepancies in the education being provided worldwide, it is imperative that data scientists and all those interested take advantage of the data publicly available to draw necessary insights into how to better the education sector in our respective countries. The purpose of this research is to showcase various analytical insights into the 2020 New York State (NYS) high school graduation rate data using various advanced database systems techniques, specifically using SQL. With these analyses, further studies and conclusions can be drawn for local governments to implement into their plans to increase the quality of the schooling system, to aim for equality for all without reg

With the increasing number of users and data on the Internet, especially social media sites, sentiment analysis topic became one of the important and essential fields for most. Collection of people's feelings and sentiment and classifying the data attracted most businesses and companies. Recently, twitter sentiment analysis has attracted much attention, because of Twitter's growth and popularity. The solution for handling enormous amounts of data from social media is a new term called Big data. Big data is not just for having a large amount of data, but also the importance of processing and the usage of the data.

This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm will extract useful features from the EEG signal. The performances of the ensemble machine learning methods are measured with accuracy, F-measure, and the area under the receiver operating characteristic (ROC) curve (AUC). EEG-based focus area localization with the proposed methods reaches 98.9% accuracy using the Rotation Forest classifier. Therefore, our results suggest that ensemble machine learning methods can be applied to differentiate the EEG signals from epileptogenic brain areas and signals recorded from non-epileptogenic brain regions with high accuracy.

The main aim of the study is to develop a real-time epilepsy prediction approach by using the ensemble machine learning techniques that might predict offline seizure paradigms. The proposed seizure prediction algorithm is patient-specific since generalization showed no satisfactory results in our previous studies. The algorithm is tested on CHB-MIT database comprised of EEG data from pediatric epileptic patients. Based on relations to number of seizures and number of files, gender and age, three patients have been chosen for this study. The special majority voting algorithm is proposed and used for raising an alarm of upcoming seizure. EEG signals are denoised using MSPCA (Multiscale PCA), the features were extracted by WPD (wavelet packet decomposition), and EEG signals were classified using Rotation Forest. The significance of the study lies in the fact that the proposed seizure prediction algorithm could be used in novel diagnostic and therapeutic applications for pediatric patients.

In this era, big data applications including biomedical are becoming attractive as the data generation and storage is increased in the last years. The big data processing to extract knowledge becomes challenging since the data mining techniques are not adapted to the new requirements. In this study, we analyse the EEG signals for epileptic seizure detection in the big data scenario using Rotation Forest classifier. Specifically, MSPCA is used for denoising, WPD is used for feature extraction and Rotation Forest is used for classification in a MapReduce framework to correctly predict the epileptic seizure. This paper presents a MapReduce-based distributed ensemble algorithm for epileptic seizure prediction and trains a Rotation Forest on each dataset in parallel using a cluster of computers. The results of MapReduce based Rotation Forest show that the proposed framework reduces the training time significantly while accomplishing a high level of performance in classifications.