This book comprises select proceedings of the international conference ETAEERE 2020, and covers latest research in the areas of electronics, communication and computing. The book includes different approaches and techniques for specific applications using particle swarm optimization, Otsu's function and harmony search optimization algorithm, DNA-NAND gate, triple gate SOI MOSFET, micro-Raman and FTIR analysis, high-k dielectric gate oxide, spectrum sensing in cognitive radio, microstrip antenna, GPR with conducting surfaces, energy efficient packet routing, iBGP route reflectors, circularly polarized antenna, double fork shaped patch radiator, implementation of Doppler radar at 24 GHz, iris image classification using SVM, digital image forgery detection, secure communication, spoken dialog system, and DFT-DCT spreading strategies. Given the range of topics covered, this book can be useful for both students and researchers working in electronics and communication.

This book comprises select proceedings of the international conference ETAEERE 2020, and covers latest research in the areas of electronics, communication and computing. The book includes different approaches and techniques for specific applications using particle swarm optimization, Otsu's function and harmony search optimization algorithm, DNA-NAND gate, triple gate SOI MOSFET, micro-Raman and FTIR analysis, high-k dielectric gate oxide, spectrum sensing in cognitive radio, microstrip antenna, GPR with conducting surfaces, energy efficient packet routing, iBGP route reflectors, circularly polarized antenna, double fork shaped patch radiator, implementation of Doppler radar at 24 GHz, iris image classification using SVM, digital image forgery detection, secure communication, spoken dialog system, and DFT-DCT spreading strategies. Given the range of topics covered, this book can be useful for both students and researchers working in electronics and communication.

All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.

All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.