scholarly journals Demand Supply Oriented Taxi Suggestion System for Vehicular Social Networks with Fuel Charging Mechanism

2019 ◽  
pp. 38-44
Author(s):  
Selvi C ◽  
Keerthana D
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Large Scale ◽  
Recommendation System ◽  
Important Research ◽  
Taxi Drivers ◽  
Charging Station ◽  
Gps Trajectories ◽  
Charging Mechanism ◽  
Supply Level

Data mining depends on large-scale taxi traces is an important research concepts. A vital direction for analyzing taxi GPS dataset is to suggest cruising areas for taxi drivers. The project first investigates the real-time demand-supply level for taxis, and then makes an adaptive tradeoff between the utilities of drivers and passengers for different hotspots. This project constructs a recommendation system by jointly considering the profits of both drivers and passengers. At last, the qualified candidates are suggested to drivers based on analysis. The project also provides a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. By combining each EV taxi’s historical recharging actions and real-time GPS trajectories, the present operational state of each taxi is predicted. Based on this information, for an EV taxi requesting a recommendation, recommend a charging station that leads to the minimal total time before its recharging starts.

A Privacy Preserving Data Mining Methodology for Dynamically Predicting Emerging Human Threats

2013 ◽  
Author(s):  
Gautam Manohar ◽  
Conrad Tucker
Keyword(s):  
Data Mining ◽  
Public Space ◽  
Real Time ◽  
Large Scale ◽  
Body Movement ◽  
Privacy Preserving ◽  
Emergent Behavior ◽  
Coordinate Space ◽  
Surveillance Systems ◽  
Privacy Preserving Data Mining

This paper proposes a privacy preserving data mining driven methodology for predicting emerging human threats in a public space by capturing large scale, real time body movement data (spatial data represented in X, Y, Z coordinate space) using Red-Green-Blue (RGB) image, infrared depth and skeletal image sensing technology. Unlike traditional passive surveillance systems (e.g., CCTV video surveillance systems), multimodal surveillance technologies have the ability to capture multiple data streams in a real time dynamic manner. However, mathematical models based on machine learning principles are needed to convert the large-scale data into knowledge to serve as a decision support system for autonomously predicting emerging threats, rather than just recording and observing them as they occur. To this end, the authors of this work present a privacy preserving data mining driven methodology that captures emergent behavior of individuals in a public space and classifies them as a threat or not a threat, based on the underlying body movements through space and time. An audience in a public environment is presented as the case study for this paper with the aim of classifying individuals in the audience as threats (or not), based on their temporal body behavior profiles.

New Information in Trending Topics of Tweets by Labelled Clusters

2015 ◽  
Vol 14 (03) ◽  
pp. 1550019
Author(s):  
Amina Madani ◽  
Omar Boussaid ◽  
Djamel Eddine Zegour
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Exponential Growth ◽  
Research Topic ◽  
Important Research ◽  
New Approach ◽  
New Information ◽  
Important Research Topic ◽  
Trending Topics ◽  
The Web

Twitter is a popular micro-blogging service, and one of the main means of spreading ideas and information throughout the web. In this system, participants post short status messages called tweets that are often available publicly. Recently, the exponential growth of tweets has started to draw the attention of researchers from various disciplines. Numerous research approaches in the data mining field have examined Twitter. How to automatically extract useful information from tweets has therefore become an important research topic. The aim of this paper is to bring up what's up which is a new approach of tweets mining. It is a more general approach that discovers many different trending topics from tweets in real-time. Trending topics have generated big interest not only for the users of Twitter but also for information seekers. Our trending topics are detected for a specific geographic town and compared with the top trending topics shown on Twitter. They are presented by labelled clusters that constitute an accurate description of each trending topic. Each cluster is labelled by an emerging trending topic and is composed of keywords that represent the properties of the trending topic.

Data Mining for Discovering Effective Time-Series Transition of Learning Strategies on Mutual Viewing-Based Learning

2018 ◽  
Vol 22 (7) ◽  
pp. 1046-1055
Author(s):  
Yuto Omae ◽  
Tatsuro Furuya ◽  
Kazutaka Mizukoshi ◽  
Takayuki Oshima ◽  
Norihisa Sakakibara ◽  
...  
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Time Series ◽  
Random Forest ◽  
Real Time ◽  
Learning Strategies ◽  
Recommendation System ◽  
Classification Model ◽  
Transition Diagram ◽  
Educational Method

We aim to develop a real-time feedback system of learning strategies during lesson time to improve academic achievement. It has been known that mutual viewing-based learning is an effective educational method. However, even though mutual viewing is an effective lesson style, there are effective or ineffective learning strategies in the learners’ individual activities. In general, the method of evaluating learning strategies is a questionnaire survey. However, the questionnaire cannot measure the learning strategies in real time. Thus, it is difficult to detect the students who use ineffective learning strategies during lesson time in real time. Recently, a system that can measure the learning strategies in real time has been developed. Using this system, it is possible to detect students who use ineffective learning strategies during lesson time on the mutual viewing-based learning. From this point of view, we aim to develop a recommendation system for real-time learning strategies for teachers and students to achieve a highly educational effect. For this purpose, we must know the features of effective or ineffective learning strategies via a system that can measure learning strategies. In this paper, we report the discovery of features of effective or ineffective learning strategies based on the data-mining approach using thek-means method, transition diagram, and random forest. We classified the time-series learning strategies over 40 min into 216 strategies and surveyed the improvement probability of academic achievement via a random-forest-based classification model. By embedding our results into the system, we may be able to automatically detect students who use ineffective learning strategies and recommend effective learning strategies.

Personalized Web Information Recommendation Based on Data Mining

2011 ◽  
Vol 225-226 ◽  
pp. 546-549 ◽  
Author(s):  
Bo He
Keyword(s):  
Data Mining ◽  
Recommendation System ◽  
Experimental Results ◽  
User Profiles ◽  
Important Research ◽  
Web Information ◽  
Research Task ◽  
Information Recommendation ◽  
Recommendation Strategy

Personalized web information recommendation service had becoming an important research task increasingly as the time goes by. This paper established user profiles and put forward a recommendation strategy. On the base of these, the paper designed a personalized web information recommendation system based on data mining, namely, PWIRS. The experimental results indicate that the recommendation strategy of PWIRS is feasible.

Real-Time Charging Station Recommendation System for Electric-Vehicle Taxis

2016 ◽  
Vol 17 (11) ◽  
pp. 3098-3109 ◽  
Author(s):  
Zhiyong Tian ◽  
Taeho Jung ◽  
Yi Wang ◽  
Fan Zhang ◽  
Lai Tu ◽  
...  
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Recommendation System ◽  
Charging Station

Execution and Resource Management in QoS-Aware Virtualized Infrastructures

2011 ◽  
pp. 200-217
Author(s):  
Dominik Lamp ◽  
Sören Berger ◽  
Manuel Stein ◽  
Thomas Voith ◽  
Tommaso Cucinotta ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Research Literature ◽  
Real Time Systems ◽  
Important Research ◽  
Large Scale Systems ◽  
Service Oriented ◽  
Execution Environment ◽  
Intensive Investigation ◽  
Time Systems

Both real-time systems and virtualization have been important research topics for quite some time now. Having competing goals, research on the correlation of these topics has started only recently. This chapter overviews recent results in the research literature on virtualized large-scale systems and soft real-time systems. These concepts constitute the fundamental background over which the execution environment of any large-scale service-oriented real-time architecture for highly interactive, distributed, and virtualized applications will be built in the future. While many aspects covered in this chapter have already been adopted in commercial products, others are still under intensive investigation in research labs all over the world.

Pricing-aware Real-time Charging Scheduling and Charging Station Expansion for Large-scale Electric Buses

2020 ◽  
Vol 12 (1) ◽  
pp. 1-26
Author(s):  
Guang Wang ◽  
Zhihan Fang ◽  
Xiaoyang Xie ◽  
Shuai Wang ◽  
Huijun Sun ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Charging Station

Survey of Data Mining Techniques Used for Real Time Churn Prediction

2017 ◽  
Vol 7 (3) ◽  
pp. 219-222 ◽  
Author(s):  
Jean Claude Turiho ◽  
◽  
Wilson Cheruiyot ◽  
Anne Kibe ◽  
Irénée Mungwarakarama ◽  
...  
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Churn Prediction ◽  
Data Mining Techniques

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

Sign in / Sign up

Export Citation Format

Share Document