{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":22,"n_cols":5,"rows_kept":22,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[16,2,16,3],[17,2,17,3],[19,2,19,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910767546503321"]
[null,null,"Titolo",null,"Applied Nature-Inspired Computing: Algorithms and Case Studies / / edited by Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020"]
[null,null,"ISBN",null,"981-13-9263-3"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (281 pages)"]
[null,null,"Collana",null,"Springer Tracts in Nature-Inspired Computing, , 2524-552X"]
[null,null,"Disciplina",null,"006.38"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Computer science—Mathematics"]
[{"$nulls":4},"Computer simulation"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Algorithm Analysis and Problem Complexity"]
[{"$nulls":4},"Mathematics of Computing"]
[{"$nulls":4},"Simulation and Modeling"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Chapter 1. Particle Swarm Optimization of Morphological Filters for Electrocardiogram Baseline Drift Estimation -- Chapter 2. Detection of Breast Cancer using Fusion of MLO and CC View Features Through a Hybrid Technique Based on Binary Firefly algorithm and Optimum Path Forest Classification -- Chapter 3. Recommending Healthy Personalized Daily Menus – A Cuckoo Search based Hyper-Heuristic Approach -- Chapter 4. A Hybrid Bat-Inspired Algorithm for Power Transmission Expansion Planning on a Practical Brazilian Network -- Chapter 5. An Application of Binary Grey Wolf Optimizer (BGWO) variants for Unit Commitment Problem -- Chapter 6. Sensorineural hearing loss identification via discrete wavelet packet entropy and cat swarm optimization -- Chapter 7. Chaotic Variants of Grasshopper Optimisation Algorithm and their application to Protein Structure Prediction -- Chapter 8. Examination of Retinal Anatomical Structures – A Study with Spider Monkey Optimization Algorithm -- Chapter 9. Nature-Inspired Metaheuristics Search Algorithms for Solving the Economic Load Dispatch Problem of Power System: A Comparative Study -- Chapter 10. Parallel-series System Optimization by Weighting Sum Methods and Nature-inspired Computing -- Chapter 11. "]
[{"$nulls":4},"Development of Artificial Neural Networks trained by Heuristic Algorithms for Prediction of Exhaust Emissions and Performance of a Diesel Engine Fuelled with Biodiesel Blends."]
[null,null,null,"Sommario/riassunto","This book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each. Moreover, the book provides a brief outline of the integration of nature-inspired computing techniques and various computational intelligence paradigms, and highlights nature-inspired computing techniques in a range of applications, including: evolutionary robotics, sports training planning, assessment of water distribution systems, flood simulation and forecasting, traffic control, gene expression analysis, antenna array design, and scheduling/dynamic resource management."]