i-JEN: Visual interactive Malaysia crime news retrieval system

Supporting crime news investigation involves a mechanism to help monitor the current and past status of criminal events. We believe this could be well facilitated by focusing on the user interfaces and the event crime model aspects. In this paper we discuss on a development of Visual Interactive Malaysia Crime News Retrieval System (i-JEN) and describe the approach, user studies and planned, the system architecture and future plan. Our main objectives are to construct crime-based event; investigate the use of crime-based event in improving the classification and clustering; develop an interactive crime news retrieval system; visualize crime news in an effective and interactive way; integrate them into a usable and robust system and evaluate the usability and system performance. The system will serve as a news monitoring system which aims to automatically organize, retrieve and present the crime news in such a way as to support an effective monitoring, searching, and browsing for the target users groups of general public, news analysts and policemen or crime investigators. The study will contribute to the better understanding of the crime data consumption in the Malaysian context as well as the developed system with the visualisation features to address crime data and the eventual goal of combating the crimes

CRIME RATE PREDICTION USING THE RANDOM FOREST ALGORITHM

  An act that creates crimes punishable by law is characterized as a crime. Rape, fraud, terrorism, kidnapping, burglary, murder, and other crimes are common in Nigeria. Examples are cybercrime, bribery and corruption, robbery, money laundering, among other crimes. Crime is a harmful and widespread social issue that affects individuals all around the world. The rate of crime has risen dramatically in recent years. To cut down on crime, at any rate, law enforcements must take preventative actions. To protect society against crime, modern systems and new technologies are required. Although accurate real-time crime study is on aid in reducing crime rates, they are nonetheless useless. As crime occurrences are dependent on, this is a difficult subject for the scientific community to solve. Therefore, this paper proposes machine learning algorithm to indicate the frequency and pattern of crimes based on the data collected and to show the extent of crime in a particular region. Various visualization approaches and machine learning algorithms are used in this study to anticipate the crime distribution over a large area. In the first stage, raw datasets were processed and visualized according to the requirements. Then, to extract knowledge from these massive datasets, machine learning methods were deployed and uncover hidden patterns in the data, which were then utilized to investigate and report on crime patterns, It is beneficial to crime analysts. Investigate these crime networks using a variety of interactive crime visualizations. As a result, it is helpful in crime prevention

SURVEY ON ADVISOR INTELLIGENCE THROUGH PURCHASE PATTERNS AND SALES ANALYTICS

In mutual fund, an individual or a firm that is in the business of giving advice about securities to clients is an investment advisor. Investment advisers are individuals or firms that receive compensation for giving advice on investing in stocks, bonds, mutual funds, or exchange-traded funds. Investment advisors manage portfolios of securities. Advisors can use new cognitive and analytics capabilities to better understand their clients and needs and have a stronger ability to deepen relationships with a better portfolio. In this paper, we analyze data points foreach advisor, and distinguish the best prospects, obtain insight into their experience and credentials, and learn about their portfolio, in other words, to recognize the pattern of portfolio of the advisors. Such analysis helps the sales people to sell the fund company products to the suitable advisors based on the nature of the product they want to sell. This is done by investigating what kind of products advisors have been buying, and what kind of products they might be looking for. This helps to increase the sales of the products as sales people will be reaching the appropriate advisors

Time series forecasting on crime data in Amsterdam for a software company

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn recent years, there have been many discussions of data mining technology implementation in the fight against terrorism and crime. Sentient as a software company has been supporting the police for years by applying data mining techniques in the DataDetective application (Sentient, 2017). Experimenting with various types of predictive model solutions, selecting the most efficient and promising solution are the objectives of this internship. Initially, extended literatures were reviewed in the field of data mining, crime analysis and crime data mining. Sentient provided 7 years of crime data which was aggregated on daily basis to create a univariate dataset. Also, an incidence type daily aggregation was done to create a multivariate dataset. The prediction length for each solution was 7 days. The experiments were divided into two major categories: Statistical models and neural network models. Neural networks outperformed statistical models for the crime data. This paper provides the overview of statistical models and neural network models used. A comparative study of all the models on similar dataset gives a clear picture of their performance on available data and generalization capability. Evidently, the experiments showed that Gated Recurrent units (GRU) produced better prediction in comparison to other models. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in DataDetective