001482341 000__ 05689cam\\2200625\i\4500 001482341 001__ 1482341 001482341 003__ OCoLC 001482341 005__ 20231128003332.0 001482341 006__ m\\\\\o\\d\\\\\\\\ 001482341 007__ cr\cn\nnnunnun 001482341 008__ 231013s2023\\\\sz\a\\\\o\\\\\000\0\eng\d 001482341 019__ $$a1402200935 001482341 020__ $$a9783031426858$$q(electronic bk.) 001482341 020__ $$a3031426851$$q(electronic bk.) 001482341 020__ $$z9783031426841 001482341 020__ $$z3031426843 001482341 0247_ $$a10.1007/978-3-031-42685-8$$2doi 001482341 035__ $$aSP(OCoLC)1402750631 001482341 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dOCLCO$$dOCLCF 001482341 049__ $$aISEA 001482341 050_4 $$aQA76.9.A43 001482341 08204 $$a519.6$$223/eng/20231013 001482341 24500 $$aMetaheuristics and optimization in computer and electrical engineering.$$nVolume 2,$$pHybrid and improved algorithms /$$cNavid Razmjooy, Noradin Ghadimi, Venkatesan Rajinikanth, editors. 001482341 24630 $$aHybrid and improved algorithms 001482341 264_1 $$aCham :$$bSpringer,$$c[2023] 001482341 264_4 $$c©2023 001482341 300__ $$a1 online resource (viii, 491 pages) :$$billustrations (chiefly color). 001482341 336__ $$atext$$btxt$$2rdacontent 001482341 337__ $$acomputer$$bc$$2rdamedia 001482341 338__ $$aonline resource$$bcr$$2rdacarrier 001482341 4901_ $$aLecture notes in electrical engineering,$$x1876-1119 ;$$vvolume 1077 001482341 5050_ $$aA Comprehensive Survey of Meta-heuristic Algorithms -- Order Reduction of the Time-independent Linear Systems Using the Firefly Algorithm with Neighbourhood Attraction -- Intelligent Voltage Control of Electric Vehicles to Manage Power Quality Problems Using Improved Weed Optimization Algorithm -- Apple Spots and Defects Detection Based on Machine Vision, Fuzzy Systems, and Improved Gray Wolf Optimization Algorithm -- Technical and Economic Evaluation of the Optimal Placement of Fuel Cells in the Distribution System of Petrochemical Industries Based on Improved Firefly Algorithm -- Modeling and Optimal Control of Power System Frequency Load Controller by Applying Disturbance in the System by a Modified Version of Firefly Algorithm -- Design of a System for Melanoma Diagnosis Using Image Processing and Hybrid Optimization Techniques -- Multi-criteria Building Performance Optimization by Mm-based Iaso Method: A Case Study -- A Chameleon Swarm Optimization Model for the Optimal Adjustment of Retrofit Values in Spanish Houses -- Brain Tumor Segmentation Based on Zernike Moments, Enhanced Ant Lion Optimization, and Convolutional Neural Network in Mri Images -- Enhancing Cyber- Physical Resiliency Based on Meta-heuristic Algorithms for Microgrids Against Malicious Attacks -- An Optimized Combination of Spectral and Spatial Features for Hyperspectral Images Classification via Arithmetic Optimization Algorithm -- Multi-objective Optimization Using the Simulation of Net-zero Energy Residential Buildings with the African Vulture Optimizer -- A Systematic Literature Survey in Alzheimer Disease Using Optimization Techniques -- A Survey on Optimization Methods Used for Early Prediction and Diagnosis of Schizophrenia Disorder -- Serially Fused Dual-deep-features Based Chest X-ray Classification Scheme to Detect Tuberculosis -- Chaotic-moth-flame-algorithm Based Scheme to Design Pid Controller for Benchmark Avr. 001482341 506__ $$aAccess limited to authorized users. 001482341 520__ $$aThis book discusses different methods of modifying the original metaheuristics and their application in computer and electrical engineering. As the race to develop advanced technology accelerates, a new era of "metaheuristics" has emerged. Through researched-based techniques and collaborative problem-solving, this book helps engineers to find efficient solutions to their engineering challenges. With the help of an expert guide and the collective knowledge of the engineering community, this comprehensive guide shows readers how to use machine learning and other AI techniques to reinvent smart engineering. From understanding the fundamentals to mastering the latest metaheuristics models, this guide provides with the skills and knowledge that need to stay ahead in the technology race. In the previous volume, authors focused on the application of original metaheuristics on electrical and computer sciences. This volume learns how AI and modified metaheuristics can be used to optimize algorithms and create more efficient electrical engineering designs. It gets insights on how data can be effectively processed and discover new techniques for creating sophisticated automation systems. It maximizes the potential of readers’ computer and electrical engineering projects with powerful metaheuristics and optimization techniques. 001482341 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 13, 2023). 001482341 650_6 $$aMétaheuristiques. 001482341 650_6 $$aOptimisation mathématique. 001482341 650_6 $$aGénie électrique. 001482341 650_0 $$aMetaheuristics. 001482341 650_0 $$aMathematical optimization.$$vCongresses$$0(DLC)sh2008107548 001482341 650_0 $$aElectrical engineering.$$0(DLC)sh 85041575 001482341 655_0 $$aElectronic books. 001482341 7001_ $$aRazmjooy, Navid,$$d1987-$$eeditor. 001482341 7001_ $$aGhadimi, Noradin,$$eeditor. 001482341 7001_ $$aRajinikanth, Venkatesan,$$eeditor. 001482341 77608 $$iPrint version: $$z3031426843$$z9783031426841$$w(OCoLC)1390875053 001482341 830_0 $$aLecture notes in electrical engineering ;$$vv. 1077.$$x1876-1119 001482341 852__ $$bebk 001482341 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-42685-8$$zOnline Access$$91397441.1 001482341 909CO $$ooai:library.usi.edu:1482341$$pGLOBAL_SET 001482341 980__ $$aBIB 001482341 980__ $$aEBOOK 001482341 982__ $$aEbook 001482341 983__ $$aOnline 001482341 994__ $$a92$$bISE