scholarly journals Models for Efficient Utilization of Resources for Upgrading Android Mobile Technology

2022 ◽  
Vol 11 (2) ◽  
pp. 1-22
Author(s):  
Abha Jain ◽  
Ankita Bansal
Keyword(s):  
Mobile Phones ◽  
Operating Systems ◽  
Mobile Technology ◽  
Prediction Models ◽  
High Dimensionality ◽  
Careful Attention ◽  
Huge Amount ◽  
Efficient Utilization ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

The need of the customers to be connected to the network at all times has led to the evolution of mobile technology. Operating systems play a vitol role when we talk of technology. Nowadays, Android is one of the popularly used operating system in mobile phones. Authors have analysed three stable versions of Android, 6.0, 7.0 and 8.0. Incorporating a change in the version after it is released requires a lot of rework and thus huge amount of costs are incurred. In this paper, the aim is to reduce this rework by identifying certain parts of a version during early phase of development which need careful attention. Machine learning prediction models are developed to identify the parts which are more prone to changes. The accuracy of such models should be high as the developers heavily rely on them. The high dimensionality of the dataset may hamper the accuracy of the models. Thus, the authors explore four dimensionality reduction techniques, which are unexplored in the field of network and communication. The results concluded that the accuracy improves after reducing the features.

Fetching Address Book Info of iPhone OS

2011 ◽  
Vol 204-210 ◽  
pp. 1266-1269 ◽  
Author(s):  
Zhi Feng Luo ◽  
Chao Sun ◽  
Shun Xiang Wu
Keyword(s):  
Operating System ◽  
Mobile Phones ◽  
Operating Systems ◽  
Mobile Technology ◽  
Mobile Communications ◽  
Rapid Development ◽  
Smart Phone ◽  
Output File ◽  
New Era ◽  
Address Book

With the rapid development of mobile technology, the mobile phones has gradually become an indispensable part in everybody’s life, and it is replacing the computer’s position step by step. The iPhone is a smart phone made of Apple Company, which opens a new era of software for mobile devices. At the same time, iOS(iPhone’s operating system) has become one of most competitive mobile communications operating systems. The Apple’s protection for security makes the iPhone owners hardly control their phone through the root (highest authority). It is so difficult to manage the information effetely with iPhone, such as contacts’ information or SMS. The paper introduced two approaches to get the contacts’ information in iPhone and restore them with vCard. At last, we managed the output file on MAC.

Efficient Retrieval Technique for Microarray Gene Expression

2012 ◽  
Vol 2 (2) ◽  
pp. 43-51
Author(s):  
J. Jacinth Salome
Keyword(s):  
Dimensionality Reduction ◽  
Microarray Data ◽  
High Dimensionality ◽  
Microarray Gene Expression ◽  
Reduction Techniques ◽  
Retrieval Technique ◽  
Efficient Retrieval ◽  
Dimensionality Reduction Techniques ◽  
Microarray Gene ◽  
Gene Data

The DNA mciroarray gene data is in the expression levels of thousands of genes for a small amount of samples. From the microarray gene data, the process of extracting the required knowledge remains an open challenge. Acquiring knowledge is the intricacy in such types of gene data, though number of researches is arising in order to acquire information from these gene data. In order to retrieve the required information, gene classification is vital; however, the task is complex because of the data characteristics, high dimensionality and smaller sample size. Initially, the dimensionality diminution process is carried out in order to shrink the microarray data without losing information with the aid of LPP and PCA techniques and utilized for information retrieval. In this paper, we propose an effective gene retrieval technique based on LPP and PCA called LPCA. The technique like LPP and PCA is chosen for the dimensionality reduction for efficient retrieval of microarray gene data. An application of microarray gene data is included with classification by SVM. SVM is trained by the dimensionality reduced gene data for effective classification. A comparative study is made with these dimensionality reduction techniques.

Dimensionality reduction techniques to analyze heating systems in buildings

2015 ◽  
Vol 294 ◽  
pp. 553-564 ◽  
Author(s):  
Manuel Domínguez ◽  
Serafín Alonso ◽  
Antonio Morán ◽  
Miguel A. Prada ◽  
Juan J. Fuertes
Keyword(s):  
Dimensionality Reduction ◽  
Heating Systems ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

scholarly journals A generalization of t-SNE and UMAP to single-cell multimodal omics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Van Hoan Do ◽  
Stefan Canzar
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cell Types ◽  
High Dimensional ◽  
Omics Data ◽  
Relative Contribution ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Concise Representation ◽  
Cellular Identity

AbstractEmerging single-cell technologies profile multiple types of molecules within individual cells. A fundamental step in the analysis of the produced high-dimensional data is their visualization using dimensionality reduction techniques such as t-SNE and UMAP. We introduce j-SNE and j-UMAP as their natural generalizations to the joint visualization of multimodal omics data. Our approach automatically learns the relative contribution of each modality to a concise representation of cellular identity that promotes discriminative features but suppresses noise. On eight datasets, j-SNE and j-UMAP produce unified embeddings that better agree with known cell types and that harmonize RNA and protein velocity landscapes.

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