Naveen Kumar

Delhi Technological University

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Pankaj Chandna

National Institute of Technology Kurukshetra

Saumesh Kumar

Indian Institute of Technology Roorkee

Padam Kumar

Vinayaka Missions University

Dheeraj Joshi

Delhi Technological University

Deo Prakash Vidyarthi

Jawaharlal Nehru University

Jan-Christoph Klie

Hess (United States)

Benjamin Carterette

Photon Spot (United States)

Mradul Dubey

Biplab Kumer Sarker

University of New Brunswick

Pankaj Kumar

Jawaharlal Nehru University

Cooperative Institutions

Technical University of Darmstadt

Banaras Hindu University

Hess (United States)

University of Delaware

University of Massachusetts Amherst

Jawaharlal Nehru University

Pacific Northwest National Laboratory

Tata Consultancy Services (India)

Amazon (Germany)

Art Institute of Portland

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INCEpTION - Corpus-based Data Science from Scratch

Richard Eckart de Castilho Jan-Christoph Klie Naveen Kumar Beto Boullosa Iryna Gurevych

Scratch

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Citations (5)

A GA BASED MULTIPLE TASK ALLOCATION CONSIDERING LOAD

International Journal of High Speed Computing (2000)

Anil Kumar Tripathi Biplab Kumer Sarker Naveen Kumar Deo Prakash Vidyarthi

A Distributed Computing System (DCS) comprising networked heterogeneous processors requires ecient tasks to processor allocation to achieve minimum turnaround time and highest possible throughput. Task allocation in DCS remains an important and relevant problem attracting the attention of researchers in the discipline. A good number of task allocation algorithms have been proposed in the literature [3{9]. This algorithm considered allocation of the modules of a single task to various processing nodes and aim to minimize the turnaround time of the given task. But they did not consider execution of modules belonging to various dierent tasks (i.e. multiple tasks). In this work we have considered the number of modules that can be accepted by individual processing nodes along with their memory capacities and arrival of multiple disjoint tasks to the DCS from time to time. In this paper, a method based on genetic algorithm is developed which is memory ecient and give an optimal solution of the problem. The given simulation results also show signicant achievement in this regard.

10.1142/s0129053300000187

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Citations (39)

Addressing Bias in Generative AI: Challenges and Research Opportunities in Information Management

Information & Management (2025)

Xiahua Wei Naveen Kumar Han Zhang

10.1016/j.im.2025.104103

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Citations (1)

Integrated scheduling of part and tool in a flexible manufacturing system using modified genetic algorithm

International Journal of Systems Assurance Engineering and Management (2017)

Naveen Kumar Pankaj Chandna Dheeraj Joshi

Genetic algorithm scheduling

Flexible manufacturing system

Operator (biology)

10.1007/s13198-017-0633-5

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Citations (4)

Integrated scheduling of part, tool and automated guided vehicles in a flexible manufacturing system using modified genetic algorithm

International Journal of Industrial and Systems Engineering (2019)

Dheeraj Joshi Pankaj Chandna Naveen Kumar

Scheduling of part and tools without considering material handling devices may result into non-implementable results as the material handling system constraints affect the overall scheduling. In this paper, an attempt has been made for the simultaneous scheduling of part, tool and two automated guided vehicles (AGVs) in a flexible manufacturing system which has identical machines with no on-board tool magazine. All the tools are stored in a common tool magazine and are shared among different machines through a material handling system. Each tool type is single in number. The problem has two interrelated problems; one as loading and scheduling of part and tool and other as scheduling of AGVs. Two heuristics namely priority dispatching rules (PDRs) and modified genetic algorithm (MGA) with three parent crossover are used to find the optimal solution to the integrated scheduling problem. Both PDRs and MGA use an algorithm based on the concept of Giffler and Thompson procedure and a heuristic approach to generate the active feasible schedule. The performance of the two proposed algorithm is compared for the results of makespan. It was found that the MGA gives better results.

10.1504/ijise.2019.10022773

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Citations (2)

Parallel Implementation of Part of Speech Tagging for Text Mining Using Grid Computing

Communications in computer and information science (2011)

Naveen Kumar Saumesh Kumar Padam Kumar

Data pre-processing

Biomedical text mining

10.1007/978-3-642-22709-7_45

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Performance Analysis of PL/SQL Query Optimization Techniques

Naveen Kumar G. Mayil Muthu Kumaran Deepjot Kaur

Optimization in PL/SQL Programming is the preeminent aspect for database related applications built using PL/SQL code. PL/SQL statement/Query can be written in different ways for fetching the same output, but the most suitable technique should be considered when performance is a crucial factor, since processing time of the query may have significant variance, starting from a fraction of a second ending to hours, depending upon the method preferred. The main aim of query optimization is to find a solution that processes a specific query in minimum optimum time duration. This paper articulates different ways to optimize the performance of SQL queries and PL/SQL scripts by reducing the time complexity and number of context switches between SQL Engine and PL/SQL Engine.

PL/SQL

SQL/PSM

Stored procedure

SQL Injection

Data Transformation Services

Data definition language

Null (SQL)

User-defined function

Language Integrated Query

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Novelty as a Measure of Interestingness in Knowledge Discovery

Zenodo (CERN European Organization for Nuclear Research) (2008)

Vasudha Bhatnagar Ahmed Sultan Al-Hegami Naveen Kumar

10.5281/zenodo.1082389

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Citations (13)

Simple Rank-Based Filtering for Microblog Retrieval: Implications for Evaluation and Test Collections.

Text REtrieval Conference (2011)

Ben Carterette Naveen Kumar A. Rama Krishna Rao Dongqing Zhu

However the two systems also used different language filtering approaches and as such cannot be compared directly. Post-submission experiments suggest that the major factor influencing results is the use of a rank-based threshold. In particular, thresholding a simple query-similarity score ranking (which has also been filtered for English language results) at rank 30 gives the best overall results by the track evaluation, even though it results in only retrieving tweets that occurred days (or even weeks) before the original query. Since this seems to be “gaming” the evaluation and is clearly not in the spirit of the track, we did some analysis of the test collection to try to determine why this was the case. The majority of this paper is dedicated to that analysis. We summarize the outcomes as follows:

Rank (graph theory)

Microblogging

Similarity (geometry)

Learning to Rank

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Time Based Feedback and Query Expansion for Twitter Search

Lecture notes in computer science (2013)

Naveen Kumar Benjamin Carterette

10.1007/978-3-642-36973-5_72

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Citations (9)