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{ "item_title" : "Experimental Design of Bio-Inspired Algorithms for Optimization Problems in Industry 5.0", "item_author" : [" Sudip Mandal", "Korhan Cengiz", "S. Balamurugan "], "item_description" : "Applied Machine Learning for IoT and Data Analytics (Volume 1) is an integrated exploration of nature-inspired optimisation techniques within the emerging Industry 5.0 paradigm- Positioned at the intersection of artificial intelligence, computational intelligence, industrial engineering, and cyber-physical systems, this volume centres on human-centricity, sustainability, resilience, and intelligent automation. The book comprehensively reviews evolutionary computation, swarm intelligence, neural computation, and hybrid metaheuristics, explaining how these methods can be systematically designed, statistically validated, and benchmarked for real-world deployment. Foundational chapters address Explainable AI (XAI), statistical experimental design, ANOVA-based modelling, parameter tuning strategies, and performance evaluation frameworks. Through fifteen carefully curated chapters, the book presents practical case studies in wireless sensor networks, smart manufacturing, micro-machining, welding optimisation, renewable energy systems, motor control, wireless communications, banking automation, and advanced antenna design. Emphasis is placed on experimental rigour, benchmarking, and reproducibility-bridging the gap between theoretical advancements and industrial implementation. Key Features: -Comprehensive review of classical and hybrid bio-inspired algorithms.-Integration of optimisation techniques within the Industry 5.0 framework.-Covers Explainable AI for transparent optimisation systems with a strong focus on experimental design, ANOVA modelling, and statistical validation.-Practical case studies across manufacturing, energy, communications, and automation.-Emphasis on reproducibility and methodological rigour with forward-looking insights into AI-enhanced and explainable optimisation trends.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/889/881/9798898814106_b.jpg", "price_data" : { "retail_price" : "131.00", "online_price" : "131.00", "our_price" : "131.00", "club_price" : "131.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Experimental Design of Bio-Inspired Algorithms for Optimization Problems in Industry 5.0|Sudip Mandal

Experimental Design of Bio-Inspired Algorithms for Optimization Problems in Industry 5.0

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Overview

Applied Machine Learning for IoT and Data Analytics (Volume 1) is an integrated exploration of nature-inspired optimisation techniques within the emerging Industry 5.0 paradigm- Positioned at the intersection of artificial intelligence, computational intelligence, industrial engineering, and cyber-physical systems, this volume centres on human-centricity, sustainability, resilience, and intelligent automation.

The book comprehensively reviews evolutionary computation, swarm intelligence, neural computation, and hybrid metaheuristics, explaining how these methods can be systematically designed, statistically validated, and benchmarked for real-world deployment. Foundational chapters address Explainable AI (XAI), statistical experimental design, ANOVA-based modelling, parameter tuning strategies, and performance evaluation frameworks.

Through fifteen carefully curated chapters, the book presents practical case studies in wireless sensor networks, smart manufacturing, micro-machining, welding optimisation, renewable energy systems, motor control, wireless communications, banking automation, and advanced antenna design. Emphasis is placed on experimental rigour, benchmarking, and reproducibility-bridging the gap between theoretical advancements and industrial implementation.

Key Features:

-Comprehensive review of classical and hybrid bio-inspired algorithms.
-Integration of optimisation techniques within the Industry 5.0 framework.
-Covers Explainable AI for transparent optimisation systems with a strong focus on experimental design, ANOVA modelling, and statistical validation.
-Practical case studies across manufacturing, energy, communications, and automation.
-Emphasis on reproducibility and methodological rigour with forward-looking insights into AI-enhanced and explainable optimisation trends.

This item is Non-Returnable

Details

  • ISBN-13: 9798898814106
  • ISBN-10: 9798898814106
  • Publisher: Bentham Science Publishers
  • Publish Date: April 2026
  • Dimensions: 10 x 7 x 0.59 inches
  • Shipping Weight: 1.22 pounds
  • Page Count: 228

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