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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Computer Science and Information Technologies
Rakesh Kumar
Kurukshetra University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Optimizing requirement analysis by the use of meta-heuristic in search based software engineering Rajesh Kumar; Rakesh Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (756.035 KB) | DOI: 10.11591/ijece.v9i5.pp4336-4343

Abstract

Requirements analysis is the first phase of software development process and it is one of the main concerns of software engineers. The selection of requirements is a complex problem caused by the heterogeneity of the users and their varied interests and demands. In this paper, it is justified that their is a strong need of optimization in requirement analysis. The paper argues that requirement selection can be viewed as an application area of Search-Based Software Engineering(SBSE). The aim is to justify the claim that requirement engineering can be re-formulated as search problem to which meta-heuristic technique can be applied.
Complexity of finite state Turing machine with other domain Rajesh Kumar; Anju Jain; Rakesh Kumar
Computer Science and Information Technologies Vol 7, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i2.p196-202

Abstract

In this paper, the authors investigate and discussed the non-deterministic state complexity of certain operations on finite state Turing machine on other domain which includes partial function and natural function over an alphabet set Σ∗. It is found that in some boolean operations on said domains, the state complexity reaches up to upper bound O( √ n!). This result is complement for the operation on Kleene star-free unary and recursive languages accepted by the finite state Turing machine.
Dynamic weight adaptation in soft voting for emotion detection using neural networks Nisha Nisha; Rakesh Kumar
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10628

Abstract

Confirming elevated accuracy and speed in multi-label automatic emotion classification endures to pose extensive challenges. Old-style machine learning (ML) models are broadly used for this. However, large-scale fast embryonic textual information often obstructs their performance. Deep learning (DL) models resolve the former problem efficiently, but fine-tuning the hyperparameter entails a lot of work and experience. Ensemble learning practices offer enhanced accuracy, but classical soft voting classifiers with static weights fall short to adapt effectively to diverse data traits. To tackle this limitation, this study proposes a novel ensemble framework that employs a neural network (NN) based dynamic weight adaptation within a soft voting classifier. The model dynamically adjusts the weights of core ML classifiers based on their real-time predictive likelihood and performance statistics. This adaptive weighting suggestively enhances the model’s ability in detecting nuanced emotional expressions in text, improving responsiveness and generalization. Comprehensive experiments conducted on yardstick emotion dataset demonstrate that proposed integration of NN driven adaptive weighting within an ensemble framework outpaces traditional approaches, capturing an overall classification accuracy of approximately 98% thus offering a scalable and robust solution for real-world sentiment analysis applications.
Co-Authors Anju Jain Nisha Nisha Rajesh Kumar