DATA ANALYTICS IN SPATIAL EPIDEMIOLOGY: A SURVEY

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
Sharmila Banu Kather ◽  
BK Tripathy
Keyword(s):  
Soft Computing ◽  
Spatial Data ◽  
Data Science ◽  
Rough Set Theory ◽  
Spatial Epidemiology ◽  
Spatial Data Mining ◽  
Computing Methods ◽  
Spatial Data Analysis ◽  
Survey Paper ◽  
Soft Computing Techniques

Spatial data analysis is being used efficiently and the governments have realized that georeferenced data yields more insight with time and locations. Epidemiology is about the study of origin and distribution of diseases and dates back to the 1600s with the instance of cholera in London. Data Science has been evolving and when analyzed with Soft Computing techniques like Rough Set Theory (RST), Fuzzy Sets, Granulation Computing which encompasses the data in its original nature, results can be obtained with accrued accuracy. This survey paper highlights Spatial Data Mining methods used in the field of Epidemiology, identifies crucial challenges and discusses of the use of Soft Computing methods. 

Prediction of The Uniaxial Compressive Strength of Rocks Materials

2018 ◽  
pp. 31-96 ◽  
Author(s):  
Nurcihan Ceryan ◽  
Nuray Korkmaz Can
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
P Wave ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Artifical Neural Network ◽  
P Wave Velocity ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Direct And Indirect Methods

This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.

A Review of Soft Computing Methods Application in Rock Mechanic Engineering

2016 ◽  
pp. 1-70 ◽  
Author(s):  
Nurcihan Ceryan
Keyword(s):  
Rock Mass ◽  
Soft Computing ◽  
Rock Mechanics ◽  
Computing Methods ◽  
Rock Material ◽  
Rock Slope Stability ◽  
Rock Mechanic ◽  
Natural Rock ◽  
Soft Computing Techniques ◽  
Soft Computing Methods

Engineering behavior of rock mass is controlled by many factors, related to its nature and the environmental conditions. Determining all the parameters, ranking their weights, and clarifying their relative effects are very difficult tasks to accomplish. To overcome these difficulties, many researchers have employed soft computing methods in rock mechanics engineering. The soft computing methods have taken an important role in rock mechanics, and their abilities to address uncertainties, insufficient information and ambiguous linguistic expressions stand out in treating complex natural rock mass. This chapter briefly will review the development of soft computing techniques in rock mechanics engineering, especially in predicting of rock engineering classification system and mechanical properties of rock material and rock mass, determination weathering degree of rock material, evolution of rock performance, blasting and, rock slope stability. In addition, the future of the development and application of soft computing in rock mechanics engineering is discussed.

scholarly journals A Grid-Based Approach for Measuring Similarities of Taxi Trajectories

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3118
Author(s):  
Wei Jiao ◽  
Hongchao Fan ◽  
Terje Midtbø
Keyword(s):  
Spatial Data ◽  
Behavior Pattern ◽  
Spatial Data Mining ◽  
Spatial Data Analysis ◽  
Exact Matching ◽  
The Road ◽  
Data Volume ◽  
Grid Codes ◽  
Position Prediction ◽  
Pass Through

Similarity measurement is one of the key tasks in spatial data analysis. It has a great impact on applications i.e., position prediction, mining and analysis of social behavior pattern. Existing methods mainly focus on the exact matching of polylines which result in the trajectories. However, for the applications like travel/drive behavior analysis, even for objects passing by the same route the trajectories are not the same due to the accuracy of positioning and the fact that objects may move on different lanes of the road. Further, in most cases of spatial data mining, locations and sometimes sequences of locations on trajectories are most important, while how objects move from location to location (the exact geometries of trajectories) is of less interest. For the abovementioned situations, the existing approaches cannot work anymore. In this paper, we propose a grid aware approach to convert trajectories into sequences of codes, so that shape details of trajectories are neglected while emphasizing locations where trajectories pass through. Experiments with Shanghai Float Car Data (FCD) show that the proposed method can calculate trajectories with high similarity if these pass through the same locations. In addition, the proposed methods are very efficient since the data volume is considerably reduced when trajectories are converted into grid-codes.

A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms

2020 ◽  
pp. 1851-1885
Author(s):  
Bilal Ervural ◽  
Beyzanur Cayir Ervural ◽  
Cengiz Kahraman
Keyword(s):  
Soft Computing ◽  
Bat Algorithm ◽  
Computing Methods ◽  
Analytical Models ◽  
Grey Wolf Optimizer ◽  
Particle Swarm Algorithm ◽  
Bee Colony ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Firefly Optimization

Soft Computing techniques are capable of identifying uncertainty in data, determining imprecision of knowledge, and analyzing ill-defined complex problems. The nature of real world problems is generally complex and their common characteristic is uncertainty owing to the multidimensional structure. Analytical models are insufficient in managing all complexity to satisfy the decision makers' expectations. Under this viewpoint, soft computing provides significant flexibility and solution advantages. In this chapter, firstly, the major soft computing methods are classified and summarized. Then a comprehensive review of eight nature inspired – soft computing algorithms which are genetic algorithm, particle swarm algorithm, ant colony algorithms, artificial bee colony, firefly optimization, bat algorithm, cuckoo algorithm, and grey wolf optimizer algorithm are presented and analyzed under some determined subject headings (classification topics) in a detailed way. The survey findings are supported with charts, bar graphs and tables to be more understandable.

scholarly journals Comparison of six different soft computing methods in modeling evaporation in different climates

2016 ◽  
Author(s):  
Lunche Wang ◽  
Ozgur Kisi ◽  
Mohammad Zounemat-Kermani ◽  
Yiqun Gan
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Least Square ◽  
Support Vector ◽  
Climatic Data ◽  
Regression Methods ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Fuzzy Genetic ◽  
Different Climates

Abstract. Evaporation plays important roles in regional water resources management,terrestrial ecological process and regional climate change. This study investigated the abilities of six different soft computing methods, Multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at eight stations in different climates, air temperature (Ta), solar radiation (Rg), sunshine hours (Hs), relative humidity (RH) and wind speed (Ws) during 1961–2000 are used for model development and validation. The first part of applications focused on testing and comparing the model accuracies using different local input combinations. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations, while GRNN model performed better in Tibetan Plateau. The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. Generalized models were also developed and tested with data of eight stations. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ, CQ and HK stations.

scholarly journals A scientific review of soft-computing techniques and methods for stock market prediction

2018 ◽  
Vol 7 (2.5) ◽  
pp. 27 ◽  
Author(s):  
Muhammad R. Islam ◽  
I F.T. Al-Shaikhli ◽  
A Abdulkadir
Keyword(s):  
Text Mining ◽  
Soft Computing ◽  
Research Area ◽  
Computing Methods ◽  
Online News ◽  
Research Field ◽  
Scientific Review ◽  
Web Based ◽  
Soft Computing Techniques ◽  
Analytical Result

Information could be power if most technological approach engaged in the era of IT.  Web based text mining is one of the approach that could be analyzed in many ways through soft-computing methods and techniques. The analytical result has shown that different methods of text mining have several advantages with the gap of knowledge that is required to improve. This paper explores the performance of various methods and its impact on specific text mining field such as web based financial text analysis for stock prediction. Key research area of financial text mining is becoming one of the potential research field based on the source of online news, forums, blogs or social media.

scholarly journals Technical Approach in Text Mining for Stock Market Prediction: A Systematic Review

2018 ◽  
Vol 10 (2) ◽  
pp. 770 ◽  
Author(s):  
Mohammad Rabiul Islam ◽  
Imad Fakhri Al-Shaikhli ◽  
Rizal Bin Mohd Nor ◽  
Vijayakumar Varadarajan
Keyword(s):  
Text Mining ◽  
Empirical Research ◽  
Soft Computing ◽  
Computing Methods ◽  
Technical Approach ◽  
Meaningful Information ◽  
Soft Computing Techniques ◽  
Textual Data ◽  
Methods And Techniques ◽  
Soft Computing Methods

Text mining methods and techniques have disclosed the mining task throughout information retrieval discipline in the field of soft computing techniques. To find the meaningful information from the vast amount of electronic textual data become a humongous task for trading decision. This empirical research of text mining role on financial text analysing in where stock predictive model need to improve based on rank search method. The review of this paper basically focused on text mining techniques, methods and principle component analysis that help reduce the dimensionality within the characteristics and optimal features. Moreover, most sophisticated soft-computing methods and techniques are reviewed in terms of analysis, comparison and evaluation for its performance based on electronic textual data. Due to research significance, this empirical research also highlights the limitation of different strategies and methods on exact aspects of theoretical framework for enhancing of performance.

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