Method of Parametric Correction in Data Transformation and Approximation Problems

BACKGROUND Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and therefore, all the symptoms should be studied properly before the diagnosis. OBJECTIVE Propose an automatic prediction system for classifying cancer to malignant or benign. METHODS This paper introduces the novel strategy based on the JayaAnt lion optimization-based Deep recurrent neural network (JayaALO-based DeepRNN) for cancer classification. The steps followed in the developed model are data normalization, data transformation, feature dimension detection, and classification. The first step is the data normalization. The goal of data normalization is to eliminate data redundancy and to mitigate the storage of objects in a relational database that maintains the same information in several places. After that, the data transformation is carried out based on log transformation that generates the patterns using more interpretable and helps fulfill the supposition, and to reduce skew. Also, the non-negative matrix factorization is employed for reducing the feature dimension. Finally, the proposed JayaALO-based DeepRNN method effectively classifies cancer-based on the reduced dimension features to produce a satisfactory result. RESULTS The proposed JayaALO-based DeepRNN showed improved results with maximal accuracy of 95.97%, the maximal sensitivity of 95.95%, and the maximal specificity of 96.96%. CONCLUSIONS The resulted output of the proposed JayaALO-based DeepRNN is used for cancer classification.

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Near-memory data transformation for efficient sparse matrix multi-vector multiplication

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis ◽

10.1145/3295500.3356154 ◽

2019 ◽

Author(s):

Daichi Fujiki ◽

Niladrish Chatterjee ◽

Donghyuk Lee ◽

Mike O'Connor

Keyword(s):

Sparse Matrix ◽

Data Transformation

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bSSA: Binary Salp Swarm Algorithm with Hybrid Data Transformation for Feature Selection

IEEE Access ◽

10.1109/access.2021.3049547 ◽

2021 ◽

pp. 1-1

Author(s):

Sayar Singh Shekhawat ◽

Harish Sharma ◽

Sandeep Kumar ◽

Anand Nayyar ◽

Basit Qureshi

Keyword(s):

Feature Selection ◽

Data Transformation ◽

Salp Swarm Algorithm ◽

Hybrid Data ◽

Swarm Algorithm

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Mapping the Land Development Processes Using Data Transformation and Clustering Methods

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323510 ◽

2020 ◽

Author(s):

Pariya Pourmohammadi ◽

Donald A. Adjeroh ◽

Michael P. Strager

Keyword(s):

Land Development ◽

Data Transformation ◽

Clustering Methods ◽

Development Processes ◽

Using Data

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Data-Texts in the Sciences

Science & Education ◽

10.1007/s11191-021-00225-y ◽

2021 ◽

Author(s):

Richard Duschl ◽

Lucy Avraamidou ◽

Nathália Helena Azevedo

Keyword(s):

Science Education ◽

Learning Environments ◽

Knowledge Building ◽

Data Transformation ◽

Scientific Practices ◽

Construction Of Knowledge ◽

Alternative Approach ◽

Current Reform ◽

Design Of Learning Environments ◽

Collective Construction

AbstractGrounded within current reform recommendations and built upon Giere’s views (1986, 1999) on model-based science, we propose an alternative approach to science education which we refer to as the Evidence-Explanation (EE) Continuum. The approach addresses conceptual, epistemological, and social domains of knowledge, and places emphasis on the epistemological conversations about data acquisitions and transformations in the sciences. The steps of data transformation, which we refer to as data-texts, we argue, unfold the processes of using evidence during knowledge building and reveal the dynamics of scientific practices. Data-texts involve (a) obtaining observations/measurements to become data; (b) selecting and interpreting data to become evidence; (c) using evidence to ascertain patterns and develop models; and (d) utilizing the patterns and models to propose and refine explanations. Throughout the transformations of the EE continuum, there are stages of transition that foster the engagement of learners in negotiations of meaning and collective construction of knowledge. A focus on the EE continuum facilitates the emergence of further insights, both by questioning the nature of the data and its multiple possibilities for change and representations and by reflecting on the nature of the explanations. The shift of emphasis to the epistemics of science holds implications for the design of learning environments that support learners in developing contemporary understandings of the nature and processes of scientific practices.

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