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Meta heuristic techniques in software engineering and its applicationsMETASOFT 2022 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Meta heuristic techniques in software engineering and its applicationsedited by Mihir Narayan Mohanty ... [et al.].
Reminder of title:	METASOFT 2022 /
remainder title:	METASOFT 2022
other author:	Mohanty, Mihir Narayan.
Published:	Cham :Springer International Publishing :2022.
Description:	x, 358 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Software engineering
Online resource:	https://doi.org/10.1007/978-3-031-11713-8
ISBN:	9783031117138$q(electronic bk.)

Meta heuristic techniques in software engineering and its applicationsMETASOFT 2022 /
Meta heuristic techniques in software engineering and its applicationsMETASOFT 2022 /[electronic resource] :METASOFT 2022edited by Mihir Narayan Mohanty ... [et al.]. - Cham :Springer International Publishing :2022. - x, 358 p. :ill. (some col.), digital ;24 cm. - Artificial intelligence-enhanced software and systems engineering,v. 12731-6033 ;. - Artificial intelligence-enhanced software and systems engineering ;v. 2..

Performance analysis of Heuristic optimization algorithms for Transportation problem -- Source Code Features Based Branch Coverage Prediction using Ensemble Technique -- Implicit Methods of Multi-Factor Authentication -- Comparative Analysis of different Classifiers Using Machine Learning Algorithm for Diabetes Mellitus -- Survey on Machine Learning Techniques for Software Reliability Accuracy Prediction -- Classification of Pest in Tomato Plants using CNN -- Deep Neural Network Approach For Identifying Good Answers in Community Platforms -- Time Series Analysis of SAR-Cov-2 virus in India using Facebook's Prophet -- Model-Based Smoke Testing Approach of Service Oriented Architecture (SOA) -- Role of Hybrid Evolutionary Approaches for Feature Selection in Classification: A Review -- Evaluation of Deep Learning Models for Detecting Breast Cancer using Mammograms -- Evaluation of Crop Yield Prediction using arsenal and Ensemble Machine learning algorithms -- Notification Based Multichannel MAC (NM-MAC) Protocol for Wireless Body Area Network -- A multi Brain Tumor Classification using a Deep Reinforcement Learning Model -- A Brief Analysis on Security in Healthcare Data using Blockchain -- A Review on Test Case Selection, Prioritization and Minimization in Regression Testing -- Artificial Intelligence Advancement in Pandemic Era -- Predictive technique for Identification of Diabetes using Machine Learning -- Prognosis of Prostate Cancer Using Machine Learning -- Sign language Detection Using Tensorflow Object Detection -- Automated Test Case Prioritization using Machine Learning -- A New Approach To Solve Linear Fuzzy Stochastic Differential Equation -- An Improved Software Reliability Prediction Model by Using Feature Selection and Extreme Learning Machine -- Signal Processing Approaches for Encoded Protein Sequences in Gynaecological Cancer Hotspot Prediction: A Review -- DepNet: Deep Neural Network based model for Estimating the Crowd Count -- Dynamic Stability enhancement of Power system by Sailfish Algorithm tuned fractional SSSC control action -- Application of Machine Learning Model Based Techniques for Prediction of Heart Diseases -- Software Effort and Duration Estimation using SVM and Logistic Regression -- A framework for ranking cloud services based on an integrated BWM-Entropy-TOPSIS Method -- An Efficient and Delay-Aware Path Construction Approach Using Mobile Sink in Wireless Sensor Network -- Application of Different Control Techniques of multi-area Power Systems -- Analysis of An Ensemble Model For Network Intrusion Detection -- D2D Resource Allocation for Joint Power Control in Heterogeneous Cellular Networks -- Prediction of Covid-19 Cases in Kerala based on meteorological parameters using BiLSTM Technique.

This book discusses an integration of machine learning with metaheuristic techniques that provide more robust and efficient ways to address traditional optimization problems. Modern metaheuristic techniques, along with their main characteristics and recent applications in artificial intelligence, software engineering, data mining, planning and scheduling, logistics and supply chains, are discussed in this book and help global leaders in fast decision making by providing quality solutions to important problems in business, engineering, economics and science. Novel ways are also discovered to attack unsolved problems in software testing and machine learning. The discussion on foundations of optimization and algorithms leads beginners to apply current approaches to optimization problems. The discussed metaheuristic algorithms include genetic algorithms, simulated annealing, ant algorithms, bee algorithms and particle swarm optimization. New developments on metaheuristics attract researchers and practitioners to apply hybrid metaheuristics in real scenarios.

ISBN: 9783031117138$q(electronic bk.)

Standard No.: 10.1007/978-3-031-11713-8doiSubjects--Topical Terms:

253562
Software engineering

LC Class. No.: QA76.758 / .M47 2022

Dewey Class. No.: 005.1

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520 $a This book discusses an integration of machine learning with metaheuristic techniques that provide more robust and efficient ways to address traditional optimization problems. Modern metaheuristic techniques, along with their main characteristics and recent applications in artificial intelligence, software engineering, data mining, planning and scheduling, logistics and supply chains, are discussed in this book and help global leaders in fast decision making by providing quality solutions to important problems in business, engineering, economics and science. Novel ways are also discovered to attack unsolved problems in software testing and machine learning. The discussion on foundations of optimization and algorithms leads beginners to apply current approaches to optimization problems. The discussed metaheuristic algorithms include genetic algorithms, simulated annealing, ant algorithms, bee algorithms and particle swarm optimization. New developments on metaheuristics attract researchers and practitioners to apply hybrid metaheuristics in real scenarios.
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