Classifying Shooting Incident Fatality in New York project presentation

Nowadays, the concept of big data grows incessantly ; recent researches proved that 90% of the whole data existed on the web had been created in l ast two years. However, this growing bumped by many critical challenges resides generall y in security level; the users care about how could providers protect their privacy on their data. Access control, cryptography, and de- identification are the main search areas grouped un der a specific domain known as Privacy Preserving Data Publishing. In this paper, we bring in suggestion a new model for access control over big data using digital signature and c onfidence interval; we first introduce our work by presenting some general concepts used to bu ild our approach then presenting the idea of this report and finally we evaluate our system b y conducting several experiments and showing and discussing the results that we got

The document describes a visual analytics project analyzing traffic collision statistics in Italy. It uses an interactive dashboard with an Italy map, histograms, and sliders to filter data by year, region, and other factors. Principal component analysis is applied to reduce the dataset dimensions before representation. The dashboard allows users to gain insights through interactive exploration of quantitative relationships between variables like accident rates in different regions.

This document discusses machine learning techniques for classifying medical datasets. It provides an overview of various artificial intelligence and machine learning algorithms that have been applied for medical dataset classification, including artificial neural networks, support vector machines, k-nearest neighbors, and decision trees. The document surveys works that have used these techniques for diseases like breast cancer, heart disease, and diabetes. It also describes common pre-processing steps for medical datasets like data normalization and feature selection methods like F-score and PCA that are used to select the most important features for classification. The classification algorithms are then evaluated based on accuracy metrics like sensitivity, specificity, and accuracy.

Pattern recognition is one of the prime concepts in current technologies in both private and public sectors. The analysis and recognition of two or more patterns is a complex task due to several factors. The consideration of two or more patterns requires huge space for keeping the storage media as well as computational aspect. Vector logic gives very good strategy for recognition of patterns. This paper proposes pattern recognition in multimodal authentication system with the use of vector logic and makes the computation model hard and less error rate. Using PCA two to three biometric patterns will be fusion and then various key sizes will be extracted using LU factorization approach. The selected keys will be combined using vector logic, which introduces a memory model often called Context Dependent Memory Model (CDMM) as computational model in multimodal authentication system that gives very accurate and very effective outcome for authentication as well as verification. In the verification step, Mean Square Error (MSE) and Normalized Correlation (NC) as metrics to minimize the error rate for the proposed model and the performance analysis will be presented.

This document describes a proposed system to use machine learning methods to predict crime rates and types of crimes in specific areas based on historical crime data. The system would analyze crime data collected from websites including date, location, and crime type to identify patterns. Machine learning algorithms would be trained on the data to build predictive models. The goal is to help law enforcement agencies more quickly detect, resolve, and prevent crimes by predicting where and what types of crimes may occur based on the characteristics of past crimes.

Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we have structural and semi-structural data. We will dive into deep of machine learning and data mining classification algorithms to find or predict something novel about the accident occurrences over the globe. We majorly concentrate on two classification algorithms to minify the research and task and they are very basic and important classification algorithms. SVM (Support vector machine), CNB Classifier. This discussion will be quite interesting with WEKA tool for CNB classifier, Bag of Words Identification, Word Count and Frequency Calculation.

The document presents a study on an efficient face recognition method employing support vector machines (SVM) and biomimetic uncorrelated local difference projection (BU-LDP). The study proposes using BU-LDP, which is based on uncorrelated local projection but uses a different neighborhood coefficient calculation approach inspired by human perception. Experimental results on several datasets show that BU-LDP and its kernel variant KBU-LDP outperform state-of-the-art methods for face recognition. Future work will focus on addressing the "one sample problem" and applying the approach to unlabeled data.

The document summarizes a research paper that empirically validated several object-oriented design metrics proposed by Chidamber and Kemerer as indicators of fault-prone classes. The study analyzed 6 metrics on 180 classes from a system. Univariate analysis found 5 metrics to be significantly correlated with fault probability. Multivariate analysis using these 5 metrics achieved better prediction of faulty classes than models using traditional code metrics. The research validated that these OO design metrics can help identify fault-prone classes early in the development lifecycle.

1) The document discusses using machine learning techniques for intrusion detection in healthcare networks. It aims to build an effective intrusion detection system that can efficiently detect intrusions and provide safety for sensitive patient health information and medical data. 2) The methodology involves pre-processing the NSL-KDD dataset, training a decision tree classifier model, and using the trained model to predict intrusions. Accuracy of 90.3% was achieved using cross-validation. 3) Future work could include using all dataset features, immediately alerting administrators of attacks, and making the system multi-lingual. The system aims to provide secure access of healthcare data for authorized users and detect unauthorized access attempts.

The recent blast of dataset size, in number of records and in addition of attributes, has set off the improvement of various big data platforms and in addition parallel data analytic algorithms. In the meantime however, it has pushed for the utilization of data dimensionality reduction systems. Mobile Telecom Industry competition has become more and more fierce. In order to improve their services and business in the competitive world, they are ready to analyse the stored data by several data mining technologies to retain customers and maintain their relationship with them. Mobile Call Detail Record (MCDR) comprises diversity and complexity information containing information like Voice Call, Text Message, Video Calls, and other Data Services usages. It is proposed to evaluate and compare the performance of different dimensionality reduction methods such as Chi-Square (Chi2) Method, Principal Component Analysis (PCA), Information Gain Attribute Evaluator, Gain-Ratio Attribute Evaluator (GRAE), Attribute Selected Classifier (ASC) and Quantile Regression (QR) Methods.

This white paper describes the analysis and models developed to predict crimes in Chicago city. Further, the models are compared and most effective and simple model is recommended with the conclusion.

ISSN 2395-650X The "International Journal of Life Sciences Biotechnology and Pharma Sciences journal appears to be a valuable resource for those interested in staying updated on the latest developments and research in these important scientific fields of Life and science journal.

This document presents a project to analyze and predict crime in San Francisco using data mining techniques. The objectives are to analyze the spatial and temporal relationships of crime, predict the category of crime in a location based on variables like location and date, and suggest safest paths between places. The authors describe using naive Bayes, decision tree, random forest and support vector machine classifiers on a dataset of over 878,000 crime incidents to classify crimes and identify patterns. Cross-validation is used to evaluate the classifiers on the training data. The results are intended to help the police department understand crime patterns and deploy resources more efficiently.

This document discusses using machine learning algorithms to predict whether users will click on advertisements based on their characteristics and behavior. It tests several algorithms on a dataset containing information about users, including Logistic Regression, Decision Trees, and Support Vector Machines. The authors preprocess the data by removing identifying location information and expanding timestamp features. They then divide the data into training and test sets to evaluate the algorithms' performance at predicting click behavior. The goal is to identify users likely to click in order to improve targeted advertising.

Predict the category of crime in a particular location on analyzing the input variables such as latitude, longitude, date, day and location.