MONITORING OF SUSPICIOUS DISCUSSIONS ON ONLINE FORUMS USING DATA MINING

Detecting Suspicion Information on Web Crime Using Crime Data Mining Techniques. International Journal of Advanced Computer Science and Information Technology (IJACSIT) Vol.-3, No. 1, 2014, Page 32-41

Analysis of Call Detail Records for Understanding Users

This section provides a brief introduction to CDRs, Neo4j and Cypher query language. 2.1 Call Detail Records. A call detail record (CDR) is a data record produced by a telephone exchange or other telecommunications equipment that documents the details of a telephone call or other telecommunications transaction (e.g., text messages) and …

Identifying user habits through data mining on call data …

This latter is the case considered for this study and it will be discussed in details in Section 3. In this paper we propose a data-mining procedure for automatically identifying the recurrent patterns in the telephonic activity of mobile network users, in order to understand and describe their habits.

Data Mining Approach for Analyzing Call Center …

As indicated above, we were able to find only results related to mining customer-related data. Some vendors of monitoring system such as eTalk and GartnerGroup built data …

What is Data Mining? Applications, Stages, and Techniques

4 stages to follow in your data mining process. 1. Data cleaning and preprocessing. Data cleaning and preprocessing is an essential step of the data mining process as it makes the data ready for analysis. Data cleaning includes deleting any unnecessary features or attributes, identifying and correcting outliers, filling in missing values, and ...

Data Mining Aspects of a Dam Monitoring Project

This data can naturally be used for data mining purposes to discover new and interesting aspects about the engineering structure. In this paper, an open source data mining tool (Weka) is briefly introduced and presented to show how data mining techniques can be applied in the handling of engineering tasks. Download to read the full chapter text.

What is Call Monitoring? Software Features + Benefits

Call monitoring lets you track and analyze customer calls and assess service interactions. Learn how to use call monitoring software for your call center!

The data mining in wireless spectrum monitoring application

With the rapid development of radio service and monitoring facilities, radio monitoring application steps into big data era. Big data analysis technology can help us get valuable information through dealing with massive monitoring data, which offer guidance to wireless spectrum resource management, abnormal signal detection, etc. In this paper, we adopt …

A comprehensive survey of data mining | International

Han et al. [] stated data mining as "data mining is a process of discovering or extracting interesting patterns, associations, changes, anomalies and significant …

Research on Environmental Monitoring System Based …

In this end, an environment monitoring system based on micro-service architecture is designed. The information management and automatic monitoring business systems are unified into a flexible, robust and efficient system platform to adapt to the big data analysis and the mining applications. Using Hadoop to build environment monitoring big data ...

Remote Sensing | Free Full-Text | Ground Subsidence Monitoring …

Ground subsidence is an important geomorphological phenomenon in mining areas. It is difficult to monitor and predict ground subsidence with high precision, especially in mountainous mining areas. Taking the mining workface of a mountainous coalfield in Taiyuan City, in the Shanxi Province of China as an example, this research …

BirdEview: Advance version of call monitoring system by …

BirdEview: Advance version of call monitoring system by using mining techniques Abstract: The main objective is to ensure the standards of every call center for its …

Supplies and Equipment Inventory, Monitoring and …

Data mining techniques are set of algorithms intended to find the hidden knowledge from the data. Usage of data mining techniques will purely depend on the problem we were going to solve. Learn more about Lead Time from ScienceDirect's AI-generated Topic Pages. Learn more about Physical Space from ScienceDirect's AI-generated Topic Pages

Data Mining and Knowledge Discovery for Process Monitoring …

The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process ...

Predictive data mining on monitoring data from the intensive …

Other data mining techniques have been developed with the purpose of dealing with time-series data directly. A recent exponent is the case of prediction in 830 patients of the need for dialysis between days 5 and 10 of ICU stay via echo-state networks, which are a form of recurrent artificial neural networks (ANN) [ 22 ].

What Is Data Mining? | Types, Methods & Examples

Learn More . Data mining involves analyzing data to look for patterns, correlations, trends, and anomalies that might be significant for a particular business. Organizations can use data mining techniques to analyze a particular customer's previous purchase and predict what a customer might be likely to purchase in the future.

(PDF) A Review on Data-Driven Process …

data-driven process monitoring from the perspective of the characterization and mining of industrial data. This paper is organized as follows: a general data-driven process moni-

Optimization of water quality monitoring programs by data mining

Association rule mining is an effective methodology to optimize water monitoring. •. Patterns in the data and automatic probes can estimate 56% of laboratory variables. •. The influence of environmental factors is significant in optimizing. •. Water quality and anthropogenic influence do not influence optimization. •.

DATA MINING AND KNOWLEDGE DISCOVERY FOR …

A data mining and knowledge discovery system can be used to improve or optimize, as well as to evaluate the behavior of the controller. Such a research and development project, called TELEMAC (Lambert 2004; Dixon et al. 2007), within the European IST program is now working on data mining which opens up the prospect of learning from data in order

What Is Data Mining? | IBM

The data mining process involves a number of steps from data collection to visualization to extract valuable information from large data sets. As mentioned above, data mining …

Data mining and clustering in chemical process databases for monitoring

In practice, such organization rarely exists and the time required to create classified training data is an obstacle to the use of advanced process monitoring strategies. Data mining and knowledge ...

Data Mining: The Process, Types, Techniques, Tools, and …

Data mining is a computational process for discovering patterns, correlations, and anomalies within large datasets. It applies various statistical analysis and machine learning (ML) techniques to extract meaningful information and insights from data. Businesses can use these insights to make informed decisions, predict trends, and …

Data mining for wearable sensors in health monitoring …

This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision …

An integrated data mining approach to real-time clinical monitoring …

In this paper, we consider an integrated data mining approach for general sudden deterioration warning. We synthesize a large feature set that includes first and second order time-series features, detrended fluctuation analysis (DFA), spectral analysis, approximative entropy, and cross-signal features.

Monitoring and analysis of mining 3D time-series deformation …

Accurate monitoring of the developing process of a surface subsidence basin is the basis of building damage assessment and surface deformation prediction. In this paper, the Synthetic Aperture Radar (SAR) data of three different imaging geometries, TerraSAR, Radarsat-2, and Sentinel-1A, were exploited.

Financial fraud detection applying data mining techniques…

A detailed description of the pros and cons of the data mining techniques found in our review has been presented in Section 5.3. Distribution of paper by data mining techniques and a classification based on their fraud types is provided in Section 5.4. Finally, Section 5.5 gives a distribution of examined papers by publication year. 5.1.

The Ultimate Guide To Call Monitoring: Tips, Tricks & FAQs

Here are some of the key benefits of using call monitoring: Capture data and insights needed to build smooth customer journeys. Avoid hefty fines by ensuring …

Machine learning in telemetry data mining of space mission: …

The development of an intelligent artificial satellite health monitoring system is a key issue in aerospace engineering that determines satellite health status and failure using telemetry data. The modern design of data mining and machine learning technologies allows the use of satellite telemetry data and the mining of integrated …

Network intrusion detection based on system calls and data mining

The method employs data mining techniques to model the normal behavior of a privileged program, and extracts normal system call sequences according to their supports and confidences in the ...

What is Data Mining?

Data warehousing is the process of storing that data in a large database or data warehouse. Data analytics is further processing, storing, and analyzing the data using complex software and algorithms. Data mining is a branch of data analytics or an analytics strategy used to find hidden or previously unknown patterns in data.

Data Mining Phone Calls | Halloo BlogHalloo Blog

Call recording can be an excellent source of training for your organization. Selecting even a few calls each week to review can help you stay in touch with the customer and help you better anticipate their …

Machine learning and data mining in manufacturing

3. Knowledge discovery process in manufacturing. The overall knowledge discovery in databases (KDD) process applied in manufacturing is given in Fig. 4. This process often includes five main steps: understanding the manufacturing domain, data preparation, machine learning/data mining, evaluation, and presentation.

Hydrology Monitoring Network: Data Mining and …

The application of data-mining techniques, including artificial neural network (ANN) models, to the Comprehensive Everglades Restoration Plan (CERP) supported databases demonstrates how empirical models of complex hydrologic systems can be developed, disparate databases and models can be integrated to support …

What Is Data Mining? How It Works, Benefits, Techniques, …

How Data Mining Works . Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It can be used in a variety of ways, such as database marketing, credit risk management, fraud detection, spam Email filtering, or even to discern the sentiment or opinion of users. The data mining process …