Novel Machine Learning for Big Data Analytics in Intelligent Support Information Management Systems

2022 ◽  
Vol 13 (1) ◽  
pp. 1-21
Author(s):  
Zhihan Lv ◽  
Ranran Lou ◽  
Hailin Feng ◽  
Dongliang Chen ◽  
Haibin Lv
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Information Management ◽  
Management System ◽  
Information Management System ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Intelligent Support ◽  
Light Gradient ◽  
Support Information

Two-dimensional 1 arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements. Scientific information technology has been developed rapidly. Here, the purposes are to make people's lives more convenient and ensure information management and classification. The machine learning algorithm is improved to obtain the optimized Light Gradient Boosting Machine (LightGBM) algorithm. Then, an Android-based intelligent support information management system is designed based on LightGBM for the big data analysis and classification management of information in the intelligent support information management system. The system is designed with modules of employee registration and login, company announcement notice, attendance and attendance management, self-service, and daily tools with the company as the subject. Furthermore, the performance of the constructed information management system is analyzed through simulations. Results demonstrate that the training time of the optimized LightGBM algorithm can stabilize at about 100s, and the test time can stabilize at 0.68s. Besides, its accuracy rate can reach 89.24%, which is at least 3.6% higher than other machine learning algorithms. Moreover, the acceleration efficiency analysis of each algorithm suggests that the optimized LightGBM algorithm is suitable for processing large amounts of data; its acceleration effect is more apparent, and its acceleration ratio is higher than other algorithms. Hence, the constructed intelligent support information management system can reach a high accuracy while ensuring the error, with apparent acceleration effect. Therefore, this model can provide an experimental reference for information classification and management in various fields.

A Survey of Big Data Analytics Using Machine Learning Algorithms

2018 ◽  
pp. 95-123 ◽  
Author(s):  
Usha Moorthy ◽  
Usha Devi Gandhi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Information Management ◽  
Big Data Analytics ◽  
High Volume ◽  
Information Management System ◽  
Research Trends ◽  
Machine Learning Algorithms ◽  
Security Issues

Big data is information management system through the integration of various traditional data techniques. Big data usually contains high volume of personal and authenticated information which makes privacy as a major concern. To provide security and effective processing of collected data various techniques are evolved. Machine Learning (ML) is considered as one of the data technology which handles one of the central and hidden parts of collected data. Same like ML algorithm Deep Learning (DL) algorithm learn program automatically from the data it is considered to enhance the performance and security of the collected massive data. This paper reviewed security issues in big data and evaluated the performance of ML and DL in a critical environment. At first, this paper reviewed about the ML and DL algorithm. Next, the study focuses towards issues and challenges of ML and their remedies. Following, the study continues to investigate DL concepts in big data. At last, the study figures out methods adopted in recent research trends and conclude with a future scope.

A Survey of Big Data Analytics Using Machine Learning Algorithms

2022 ◽  
pp. 655-677
Author(s):  
Usha Moorthy ◽  
Usha Devi Gandhi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Information Management ◽  
Big Data Analytics ◽  
High Volume ◽  
Information Management System ◽  
Research Trends ◽  
Machine Learning Algorithms ◽  
Security Issues

Big data is information management system through the integration of various traditional data techniques. Big data usually contains high volume of personal and authenticated information which makes privacy as a major concern. To provide security and effective processing of collected data various techniques are evolved. Machine Learning (ML) is considered as one of the data technology which handles one of the central and hidden parts of collected data. Same like ML algorithm Deep Learning (DL) algorithm learn program automatically from the data it is considered to enhance the performance and security of the collected massive data. This paper reviewed security issues in big data and evaluated the performance of ML and DL in a critical environment. At first, this paper reviewed about the ML and DL algorithm. Next, the study focuses towards issues and challenges of ML and their remedies. Following, the study continues to investigate DL concepts in big data. At last, the study figures out methods adopted in recent research trends and conclude with a future scope.

scholarly journals Design of University Library and Information Management System Based on Big Data Fusion

2018 ◽  
Vol 232 ◽  
pp. 01010
Author(s):  
Jia Zhang
Keyword(s):  
Big Data ◽  
Information Retrieval ◽  
Data Fusion ◽  
Information Management ◽  
Management System ◽  
Information Management System ◽  
Recall Rate ◽  
Linux Kernel ◽  
University Library ◽  
The University

In order to improve the ability of library and information management in colleges and universities, and improve intelligent retrieval level of books, a design method of library information management system is proposed based on big data fusion. The phase space reconstruction technology is used to reconstruct the feature of library and information. The feature quantity of semantic concept set of library information is extracted, and the classification storage and information retrieval of library information is carried out by fuzzy clustering method. The adaptive training method is used for feature fusion, and big data fusion of library and information is realized in high dimensional feature space. The data processing center is set up under the Linux kernel environment, the application program of the university library information management system is developed under the Linux kernel, and the VXI bus technology is used to transmit and schedule the university library information management information and data. Realize the software development and design of the school library information management system. The test results show that the design of university library information management system with this method has good information storage and scheduling ability, and it improves the performance of library information retrieval. In the information recall rate and recall rate and other indicators performance has an advantage.

Protein pKa prediction by tree-based machine learning

2021 ◽  
Author(s):  
Ada Y. Chen ◽  
Juyong Lee ◽  
Ana Damjanovic ◽  
Bernard R. Brooks
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Pka Prediction ◽  
Light Gradient ◽  
Structure Database ◽  
Gradient Boosting Machine ◽  
Extreme Gradient Boosting ◽  
Better Than ◽  
Protein Pka

We present four tree-based machine learning models for protein pKa prediction. The four models, Random Forest, Extra Trees, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), were trained on three experimental PDB and pKa datasets, two of which included a notable portion of internal residues. We observed similar performance among the four machine learning algorithms. The best model trained on the largest dataset performs 37% better than the widely used empirical pKa prediction tool PROPKA. The overall RMSE for this model is 0.69, with surface and buried RMSE values being 0.56 and 0.78, respectively, considering six residue types (Asp, Glu, His, Lys, Cys and Tyr), and 0.63 when considering Asp, Glu, His and Lys only. We provide pKa predictions for proteins in human proteome from the AlphaFold Protein Structure Database and observed that 1% of Asp/Glu/Lys residues have highly shifted pKa values close to the physiological pH.

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