An oversampling method for multi-class imbalanced data based on composite weights

To solve the oversampling problem of multi-class small samples and to improve their classification accuracy, we develop an oversampling method based on classification ranking and weight setting. The designed oversampling algorithm sorts the data within each class of dataset according to the distance from original data to the hyperplane. Furthermore, iterative sampling is performed within the class and inter-class sampling is adopted at the boundaries of adjacent classes according to the sampling weight composed of data density and data sorting. Finally, information assignment is performed on all newly generated sampling data. The training and testing experiments of the algorithm are conducted by using the UCI imbalanced datasets, and the established composite metrics are used to evaluate the performance of the proposed algorithm and other algorithms in comprehensive evaluation method. The results show that the proposed algorithm makes the multi-class imbalanced data balanced in terms of quantity, and the newly generated data maintain the distribution characteristics and information properties of the original samples. Moreover, compared with other algorithms such as SMOTE and SVMOM, the proposed algorithm has reached a higher classification accuracy of about 90%. It is concluded that this algorithm has high practicability and general characteristics for imbalanced multi-class samples.

Efficient Data Sorting Introducing a New Method

The efficiency of data sorting algorithms is the key aspect which determines the speed of data processing and searching. The best known efficiency of sorting algorithm has been Log (N) if there are N terms. All of the well-known sorting algorithms use various techniques to sort data. The basis for most of these are comparing the data terms with each other. In this manuscript, we are introducing a new approach for sorting data. This method is postulated to have the highest efficiency ever achieved by any of the sorting algorithms. We achieve this by sorting data without comparing the data terms. Or achieving results of data comparison without comparing the terms explicitly.

Implementation Of Bubble Sort Algorithm On 2 Fruit Models Of Data Selection Using The Java Program Language

There are various kinds of data sorting methods that we know of which are the Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, Shell Sort, Heap Sort, and Radix Sort methods. All of these methods have advantages and disadvantages of each, whose use is determined based on needs. Each method has a different algorithm, where different algorithms affect the execution time. One interesting algorithm to be implemented on 2 variant models of data sorting is the Bubble Sort algorithm, the reason is that this algorithm has a fairly long and detailed process flow to produce an ordered data sequence from a previously unordered data sequence. Two (2) data sorting variant models that will be implemented using the Bubble Sort algorithm are: Ascending data sorting variants moving from left to right, and Descending data sorting variants moving from left to right. And the device used in implementing the Bubble Sort algorithm is the Java programming language.

Learning Management and Its Effect on Improving the Quality of Education

The rapidly changing time has brought changes, including education, to every aspect of life. Education must constantly make changes to improve the quality of education as one of the formal educational institutions for the community. In this study the types of case studies are qualitative. Interviews and literary studies, field observations, and documentation were used for data collection techniques in this study. In this study, the analysis coincided with data collection on the ground. The investigator reduces data obtained in the field by analyzing the data, sorting each information by the focus of research and drawing conclusions in each direction. This study shows that improvements in the quality of education must be based on alternative management of schools. This promotes the development of the concept of quality management based on schools. This alternative management allows schools to be independent and regulated to improve the quality of education but still refers to national policies.

Large-Scale Data Sorting Using Independent Equal-Length Subarrays

Design an efficient data sorting algorithm that requires less time and space complexity is essential for large data sets in wireless networks, the Internet of things, data mining systems, computer science, and communications engineering. This paper proposes a low-complex data sorting algorithm that distinguishes the sorted/similar data, makes independent subarrays, and sorts the subarrays’ data using one of the popular sorting algorithms. It is proved that the mean-based pivot is as efficient as the median-based pivot for making equal-length subarrays. The numerical analyses indicate slight improvements in the elapsed time and the number of swaps of the proposed serial Merge-based and Quick-based algorithms compared to the conventional ones for low/high variance integer/non-integer uniform/Gaussian data, in different data lengths. However, using the gradual data extraction feature, the sorted parts can be extracted sequentially before ending the sorting process. Also, making independent subarrays proposes a general framework to parallel realization of sorting algorithms with separate parts. Simulation results indicate the effectiveness of the proposed parallel Merge-based and Quick-based algorithms to the conventional serial and multi-core parallel algorithms. Finally, the complexity of the proposed algorithm in both serial and parallel realizations is analyzed that shows an impressive improvement.

Outlier Detection Using Minimum Vector Variance Algorithm with Depth Function and Mahalanobis Distance

Outliers are observations where the point of observation deviates from the data pattern. The existence of outliers in the data can cause irregularities in the results of data analysis. One solution to this problem is to detect outliers using a statistical approach. The statistical approach method used in this study is the Minimum Vector Variance (MVV) algorithm which has robust characteristics for outliers. The purpose of this research is to detect outliers using the MVV algorithm by changing the data sorting criteria using the Robust Depth Mahalanobis to produce maximum detection. The results obtained from this study are that RDMMVV is superior to the observed value in showing the outliers and the location of the outliers in the data plot compared to DMMVV and MMVV.

Big Data Integration and Processing Model

The presented paper deals with data integration and sorting of Covid-19 data. The data file contains fifteen data fiels and for the design of integration and sorting model each of them is configured in data type, format and field length. For the data integration and sorting model design Talend Open Studio is used. The model concerns the performance of four main tasks: data integration, data sorting, result display, and output in .xls file format. For the sorting process two rules are assigned in accordance with the medical and biomedical requirements, namely to sort report date descending order and the Country Name field in alphabetical one

Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics

Longitudinal phase space (LPS) provides a critical information about electron beam dynamics for various scientific applications. For example, it can give insight into the high-brightness X-ray radiation from a free electron laser. Existing diagnostics are invasive, and often times cannot operate at the required resolution. In this work we present a machine learning-based Virtual Diagnostic (VD) tool to accurately predict the LPS for every shot using spectral information collected non-destructively from the radiation of relativistic electron beam. We demonstrate the tool’s accuracy for three different case studies with experimental or simulated data. For each case, we introduce a method to increase the confidence in the VD tool. We anticipate that spectral VD would improve the setup and understanding of experimental configurations at DOE’s user facilities as well as data sorting and analysis. The spectral VD can provide confident knowledge of the longitudinal bunch properties at the next generation of high-repetition rate linear accelerators while reducing the load on data storage, readout and streaming requirements.