scholarly journals Prediction Analysis of Trip Production Using Cross-Classification Technique

2006 ◽  
Vol 14 (4) ◽  
pp. 54-63
Author(s):  
Dr.Abdul Khalik Al-Tae ◽  
Amal M. Taher
Keyword(s):  
Prediction Analysis ◽  
Classification Technique ◽  
Cross Classification ◽  
Trip Production

Adjusting Trip Rates in the Cross-Classification Table by Using the Fuzzy Optimization Method

2003 ◽  
Vol 1836 (1) ◽  
pp. 76-82
Author(s):  
Shinya Kikuchi ◽  
Jongho Rhee
Keyword(s):  
Travel Demand ◽  
Fuzzy Optimization ◽  
Optimization Method ◽  
Fuzzy Linear Programming ◽  
Actual Number ◽  
Linear Programming Method ◽  
Real World Data ◽  
Cross Classification ◽  
Trip Production ◽  
Classification Table

Trip-production rates presented in cross-classification tables are essential data for the planner’s understanding of the travel characteristics of a region. Trip rates obtained from surveys, however, often show a pattern that is not consistent with what is expected by the analyst; for example, the greater the household size and auto ownership, the greater the number of trips generated. This pattern may not be found in the trip rates that are obtained directly by the survey. In such cases, analysts commonly adjust the irregularities manually. The way in which the values are adjusted affects the credibility of the trip table and, ultimately, the forecast travel demand. A method that adjusts the values of the trip table systematically is presented. The process uses the fuzzy linear programming method. The objective is to make the adjusted value as close to the observed value as possible. The constraints are to make the adjusted values adhere to the analyst’s general expectations about the pattern of the values in the table, and to match the number of trips estimated from the adjusted trip table with the actual number of trips surveyed. An application example that uses real-world data is given.

scholarly journals Predicting Diabetes by adopting Classification Approach in Data Mining

2019 ◽  
Vol 3 (2-2) ◽  
Author(s):  
Rapinder Kaur
Keyword(s):  
Data Mining ◽  
Research Work ◽  
Svm Classifier ◽  
Classification Approach ◽  
Prediction Analysis ◽  
Classification Technique ◽  
Knn Classifier ◽  
The World ◽  
Diabetes Prediction ◽  
Simulation Results

As the world is growing fast, the metamorphosing of things, lifestyle, perceptions of people and resources is taking place. But the elevation in technology has become a challenge now as the ideas, innovations are amplifying. One of the biggest things the advancement and elevations in technology has given birth is “Big Data”. In this data massive amount of information is hidden. In order to refine or process this data and to find out and unmask the insights, many techniques and algorithms have been evolved, one of which is the data mining. The data mining is the approach or procedure which helps in detaching or extracting profitable and fruitful knowledge, reports and facts from the rough or impure data. The prediction analysis is approach comprehended from data mining to forecast and figure out the future making using classification technique. This research work is based on the diabetes prediction by making use of classification approach. In the existing approach SVM classifier is applied for the prediction analysis. To increase accuracy approach of KNN classifier is applied for the prediction analysis. Both the proposed and existing methods are implemented in Python. The simulation results show that accuracy of KNN is increased and execution time is reduced.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

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