We introduce a novel method for the simultaneous estimation and localization of impact damage in composite sandwich plates using branched neural network. The dataset consists of 25 specimens, with low-velocity impact damage varying in impact energy and impact location. Fast Fourier Transform (FFT) and Power Spectral Density (PSD) of each specimen is obtained experimentally and Principal Component Analysis (PCA) is used for feature extraction. FFT features demonstrated better clusters in two-dimensional t-distributed Stochastic Neighbor Embedding (t-SNE) feature plots, providing higher model validation accuracies when compared to PSD for all the tasks. Shallow neural networks outperformed machine learning approaches providing best validation accuracies of 97.29% and 98.52% for damage estimation and damage localization tasks respectively. Branched neural networks with varying architectures have been trained and validated for the multi-output task of simultaneous impact damage estimation and localization, which provided best validation accuracies of 96.82% and 95.96% for the respective sub-tasks. � 2023 IEEE.

Digitalization in the system has brought notable changes in the marketing world, shopping trends, and customer choice, which change dynamically. To achieve corporate goals and budget in a defined span of time, digital marketing strategies are executed through digital channels. The marketing intelligence approach is a cutting edge dealing with knowledge of machine learning to provide better marketing edge support systems. ML classifiers can be used to analyse customer behaviour in the contemporary world, project the data, and generate the validity scores to choose the best algorithm for the required consumer dataset. In this chapter, the authors discuss the various ML classifiers for customer analysis and describe the possible benefits related to the application of machine learning techniques in the field of digital marketing and its management. this chapter is concluded by giving an insight on a few relevant marketing fields where concepts of machine learning like data mining, artificial intelligence, and soft computing can be applied with valuable future insights to it. � 2023, IGI Global. All rights reserved.

Analog-to-Digital Converters (ADCs) are devices that transform analog signals into digital signals and are used in various applications such as audio recording, data acquisition, and measurement systems [1]. Prior to the development of actual chip, there is a need for prototyping, testing and verifying the performance of ADCs in different scenarios. Analog macros cannot be tested on an FPGA. In order to ensure the macros function properly, the emulation of the ADC is done first. This is a digital module and can be designed in System Verilog. This paper demonstrates the design of the module on FPGA for Analog to Digital Converter (ADC) emulation. The emulation is done specific to the ADC macro which has programmable resolutions of 12/10/8/6 bit. � The Author(s).