Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Applied Nature-Inspired Computing: Algorithms and Case Studies

Applied Nature-Inspired Computing: Algorithms and Case Studies

Ashour, Amira S.
Dey, Nilanjan
Bhattacharyya, Siddhartha

Paperback

Series: Springer Tracts in Nature-Inspired Computing

Technology & EngineeringGeneral ComputersProgramming

ISBN10: 981139265X
ISBN13: 9789811392658
Publisher: Springer Nature
Published: Aug 25 2020
Pages: 275
Weight: 0.90
Height: 0.60 Width: 6.14 Depth: 9.21
Language: English
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. Development of Artificial Neural Networks trained by Heuristic Algorithms for Prediction of Exhaust Emissions and Performance of a Diesel Engine Fuelled with Biodiesel Blends.

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