scholarly journals An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 32
Author(s):  
Kiyoharu Tagawa
Keyword(s):  
Differential Evolution ◽  
Information Technologies ◽  
Flood Control ◽  
Stratified Sampling ◽  
Data Sets ◽  
Data Set ◽  
Relaxation Problem ◽  
Constrained Problems ◽  
Huge Data ◽  
Adaptive Differential Evolution

In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is available nowadays due to advanced information technologies. Since the data set is too large to evaluate the probabilistic constraint of CCP, a new data reduction method called Weighted Stratified Sampling (WSS) is proposed to describe a relaxation problem of CCP. An adaptive Differential Evolution combined with a pruning technique is also proposed to solve the relaxation problem of CCP efficiently. The performance of WSS is compared with a well known method, Simple Random Sampling. Then, the proposed approach is applied to a real-world application, namely the flood control planning formulated as CCP.

scholarly journals Sporadic <i>E</i> signatures revealed from multi-satellite radio occultation measurements

2010 ◽  
Vol 8 ◽  
pp. 225-230 ◽  
Author(s):  
C. Arras ◽  
C. Jacobi ◽  
J. Wickert ◽  
S. Heise ◽  
T. Schmidt
Keyword(s):  
Radio Occultation ◽  
Signal Amplitude ◽  
Global Scale ◽  
Data Sets ◽  
Data Set ◽  
Huge Data ◽  
Large Correlation ◽  
Sporadic E Layers ◽  
E Region ◽  
Sporadic E

Abstract. Radio occultation measurements performed by the satellites CHAMP, GRACE and FORMOSAT-3/COSMIC provide a huge data set for atmospheric and ionospheric investigations on a global scale. The data sets are used to extract information on sporadic E layers appearing in the lower ionospheric E region. This is done by analyzing signal amplitude variations of the GPS L1 signal. Sporadic E altitudes in northern midlatitudes found from radio occultation measurements are compared with ground-based ionosonde soundings. A large correlation of sporadic E altitudes from these two techniques is found.

scholarly journals An Effective Technique on Clustering in Perspective of Huge Data Set

2020 ◽  
Vol 8 (6) ◽  
pp. 4485-4491
Keyword(s):  
Data Sets ◽  
Statistical Machine Learning ◽  
Science And Engineering ◽  
Effective Technique ◽  
Data Set ◽  
Cluster Technique ◽  
Data Object ◽  
Cluster Set ◽  
Huge Data ◽  
The Mean

Analysis of data plays a crucial job considering the different phenomenon. It explores the prior knowledge, consisting of development across the extensively different communities. Cluster technique is the collecting of data object placed into groups. Therefore objects are the same nature or similar place within a cluster different nature (i.e. dissimilar) put in other cluster. Differences and likeness are refereed on the attribute values say that the objects involved in measuring distance. We have reviewed a few clustering techniques for data sets in data mining of various field of computer science and engineering, statistical, machine learning and a novel attracting field of demanding efforts. Several closely related concepts of neural network, fuzzy and genetic algorithm are also discussed. In this research paper to also discussed the facebook data set to mining the attributes from the cluster set to changing the mean square error 0-10 -6 and also discuss measuring the performance.

scholarly journals Image Processing of SEM Image Nano Silver Using K-means MATLAB Technique

2019 ◽  
Vol 29 (3) ◽  
pp. 150 ◽  
Author(s):  
Elham Jasim Mohammad
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithms ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Sem Image ◽  
Huge Data ◽  
And Performance ◽  
Biometric Information

Nanotechnology is one of the non-exhaustive applications in which image processing is used. For optimal nanoparticle visualization and characterization, the high resolution Scanning Electron Microscope (SEM) and the Atomic Force Microscope (AFM) are used. Image segmentation is one of the critical steps in nanoscale processing. There are also different ways to reach retail, including statistical approximations.In this study; we used the K-means method to determine the optimal threshold using statistical approximation. This technique is thoroughly studied for the SEM nanostructure Silver image. Note that, the image obtained by SEM is good enough to analyze more recently images. The analysis is being used in the field of nanotechnology. The K-means algorithm classifies the data set given to k groups based on certain measurements of certain distances. K-means technology is the most widely used among all clustering algorithms. It is one of the common techniques used in statistical data analysis, image analysis, neural networks, classification analysis and biometric information. K-means is one of the fastest collection algorithms and can be easily used in image segmentation. The results showed that K-means is highly sensitive to small data sets and performance can degrade at any time. When exposed to a huge data set such as 100.000, the performance increases significantly. The algorithm also works well when the number of clusters is small. This technology has helped to provide a good performance algorithm for the state of the image being tested.

A Surrogate Data-Based Approach for Validating Deep Learning Model Used in Healthcare

2020 ◽  
pp. 132-146
Author(s):  
Meenakshi Srivastava
Keyword(s):  
Deep Learning ◽  
Predictive Modelling ◽  
Surrogate Data ◽  
Learning Model ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Huge Data ◽  
Small Data Sets ◽  
Deep Learning Model

IoT-based communication between medical devices has encouraged the healthcare industry to use automated systems which provide effective insight from the massive amount of gathered data. AI and machine learning have played a major role in the design of such systems. Accuracy and validation are considered, since copious training data is required in a neural network (NN)-based deep learning model. This is hardly feasible in medical research, because the size of data sets is constrained by complexity and high cost experiments. The availability of limited sample data validation of NN remains a concern. The prediction of outcomes on a NN trained on a smaller data set cannot guarantee performance and exhibits unstable behaviors. Surrogate data-based validation of NN can be viewed as a solution. In the current chapter, the classification of breast tissue data by a NN model has been detailed. In the absence of a huge data set, a surrogate data-based validation approach has been applied. The discussed study can be applied for predictive modelling for applications described by small data sets.

scholarly journals Performance Analysis of a Faster In−place External Sorting Algorithm

2020 ◽  
pp. 1-7
Author(s):  
Asaduzzaman Nur Shuvo ◽  
Apurba Adhikary ◽  
Md. Bipul Hossain ◽  
Sultana Jahan Soheli
Keyword(s):  
Time Complexity ◽  
Divide And Conquer ◽  
Sorting Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Internal Memory ◽  
Huge Data ◽  
External Sorting ◽  
Bottle Neck ◽  
Quick Sort

Data sets in large applications are often too gigantic to fit completely inside the computer’s internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance bottle−neck. While applying sorting on this huge data set, it is essential to do external sorting. This paper is concerned with a new in−place external sorting algorithm. Our proposed algorithm uses the concept of Quick−Sort and Divide−and−Conquer approaches resulting in a faster sorting algorithm avoiding any additional disk space. In addition, we showed that the average time complexity can be reduced compared to the existing external sorting approaches.

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