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Institution

Motilal Nehru National Institute of Technology Allahabad

EducationAllahabad, Uttar Pradesh, India
About: Motilal Nehru National Institute of Technology Allahabad is a education organization based out in Allahabad, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 2475 authors who have published 5067 publications receiving 61891 citations. The organization is also known as: NIT Allahabad & Motilal Nehru Regional Engineering College.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors suggest that a major reason for the failure of many knowledge management projects is the absence of a well-defined strategic plan to guide implementation and propose a framework that can be used specifically by engineering firms to guide the KM implementation process.
Abstract: Knowledge management (KM) is the process of leveraging organizational knowledge to deliver long‐term advantage to a business and is based on a business strategy that involves engineering various knowledge‐centric business processes and developing organization structures to support these. These, in turn, require technology to capture, codify, store, disseminate and reuse the knowledge. Successful deployment of KM is not a simple process. This paper suggests that a major reason for the failure of many KM projects is the absence of a well‐defined strategic plan to guide implementation. This paper discusses the strategic planning needs of the KM deployment process, and develops a framework that could be used specifically by engineering firms to guide the KM implementation process.

60 citations

Journal ArticleDOI
TL;DR: Together, discovery and validation of new CRC patient stratification methods, screening for novel therapeutic targets, and enhanced diagnosis of CRC may improve the treatment outcome.
Abstract: Molecular subtypes-based therapies offer new potential framework for desired and precise outcome in clinical settings. Current treatment strategies in colorectal cancer are largely 'one drug fit all' model for patients that display same pathological conditions. However, CRC is a very heterogenous set of malignancy that does not support for above criteria. Each subtype displays different pathological and genetic signatures. Based on these features, therapeutic stratification for individual patients may be designed, which may ultimately lead to improved therapeutic outcomes. In this comprehensive review, we have attempted to briefly outline major CRC pathways. A detailed overview of molecular subtypes and their clinical significance has been discussed. Present and future methods, governing CRC subtyping in the era of personalized therapy with a special emphasis on CMS subtypes of CRC has been reviewed. Together, discovery and validation of new CRC patient stratification methods, screening for novel therapeutic targets, and enhanced diagnosis of CRC may improve the treatment outcome.

60 citations

Journal ArticleDOI
TL;DR: In this paper, the reduced-order models of higher-order power systems using model-order reduction techniques were derived using fractional-order proportional-integral-derivative (FOPI-D) models.
Abstract: An effort has been given to derive the reduced-order models (ROMs) of higher-order power systems using model-order reduction techniques. Fractional-order proportional-integral-derivative (FOPI-D) c...

60 citations

Journal ArticleDOI
TL;DR: In this paper, a coupled methodology comprising of Finite Element Method (FEM) and Artificial Neural Network (ANN) has been used to develop a prediction model for the Laser Beam Percussion Drilling (LBPD) process.

60 citations


Authors

Showing all 2547 results

NameH-indexPapersCitations
Santosh Kumar80119629391
Anoop Misra7038517301
Naresh Kumar66110620786
Munindar P. Singh6258020279
Arvind Agarwal5832512365
Mahendra Kumar542169170
Jay Singh513018655
Lalit Kumar4738111014
O.N. Srivastava4754810308
Avinash C. Pandey453017576
Sunil Gupta435188827
Rakesh Mishra415457385
Durgesh Kumar Tripathi371335937
Vandana Singh351904347
Prashant K. Sharma341743662
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202342
202284
2021728
2020587
2019532
2018423