Chapter 11 discusses applications of artificial neural networks in the manufacturing sector in detail. The Industry 4.0 approach, which calls for exhaustive useof computers in different sectors of industry, is discussed along with suitable diagrams. Seven different types of ANN architecture are explained, along with different types of learning techniques exhibited by neural networks. A case study involving real-time hard machining experiments is explained with the help of MATLAB software NNTool module. Finally, an optimization model is derived and explained with ANN. The chapter also describes the advantages and applications of ANN in mechanical and manufacturing technology.
Chapter 12 proposes a system for the multilingual translation of speech to text. The conversion is based on speech signal knowledge. The speech-to-text (STT) process takes as input the utterances of human speech and includes as output a string of words. The purpose of this system is to extract, classify and acknowledge speech information. The project aims to automate the application to overcome the language barrier between countries and even states throughout the world; the above program will perform the different features in the application. The application recognizes speech (human matter) in one language to communicate expressively to another language specified by the user.
Chapter 13 presents a survey on the classification of automatic summarization techniques. Searching for relevant information in summaries typically consumes less time as opposed to searching the entire collection of web pages or documents. Summary generation is helpful in many natural language processing tasks such asretrieving the relevant documents, indexing the text documents, generating personalized summaries, document classification, question and answering system. Extractive summarization techniques are easy to develop as opposed to abstractive summarization, but abstractive summarization models are capable of producing more coherent summaries than extractive methods. This chapter also includes a discussion of different types of datasets used. Intrinsic and extrinsic methods for evaluating summaries are also discussed by the authors. From this survey it is observed that different summarization techniques are found suitable for different datasets. The chapter concludes with a discussion about open research problems to be solved in automatic text summarization (ATS).
Chapter 14 proposes a framework for sentiment analysis of twitter data. The authors measured tweets posted by users in the format of hashtags (#) to state their belief about existing trends. Basically, the sentiment of tweets was investigated using Google Cloud Platform, BigQuery, and Google App Engine. Word intellect recapitulation and WordNet sign inputs were used to amplify the precision. Later, with the help of a classification method, information or data was segregated in the form of positive, negative and neutral. Significant insights are acquired by data visualization. The sentiment analysis was executed based on the ranking produced.
Chapter 15 explores the applications of topic modeling in research. The authors found that applying topic models on various applications is helpful in scientific research. With the help of topic modeling, we can generate topics that are really useful and interesting in an automated way. The literature survey shows that latent dirichlet allocation (LDA) is widely used in text analysis. Social networks are also mainly involvedin this field of text analysis. The use and applications of topic modeling research discussed in this chapter can be a significant source for text mining research, with topic modeling based on popular techniques for researchers in future works.
Part II: Optimization
Chapter 16 provides a comparison of the performance of four machine learning algorithms—Naïve Bayes, Neural Network, Support Vector Machine, and K-nearest Neighbors—in spam classification. The implementation of the algorithms is carried out in R and performance is evaluated by using AUC of the ROC curve, Accuracy, Kappa, and F-Measure. The results revealed that the SVM algorithm performed better than the other algorithms. This work showed that the receiver operating curve–area under the curve (ROC-AUC) is better suited for use in the machine learning world when compared to the accuracy metrics which are generally used in assessing the performance measurement of a classification algorithm.
Chapter 17 deals with an inventory system where urea bags of varying bulk sizes arrive at the warehouse, in which the arrivals follow the Poisson process and the inter arrival times follow exponential distribution. Probability distribution of inventory levels and total expected cost per unit time are obtained, supported with numerical calculations and graphical representations.
Chapter 18 represents a single-objective prototype for supply chain optimization considering disruption scenarios. The goal is to lessen the amount of the setup cost, shipping cost, production price, inventory expense, purchasing cost and scenario cost. A mixed integer linear programming model is developed which as a result of multiple entities is complex. The intention is to find a solution to such a model by developing a solver with the intent of providing a comparative study with different evolutionary approaches and numerical methods like branch and bound.
Chapter 19 studies the tax risk profile of South African construction companies, which is characterized by the book value, cash flow position, headcount, firm earnings, debt size and type of firm. The model that defines this tax risk is a neural network (NN) boosted generalized linear model (GLM). The main aim of this study was to develop an artificially intelligent pricing model. The study, which was conducted using an examination of financial statements of construction companies, highlights the key determinants of the price of tax risk for construction firms. Modeling techniques used to build the pricing model are discussed in detail along with their challenges.
Chapter 20 proposes a design of a simplified Type-A Schiffman phase shifter (SPS) based on microstrip transmission line (TL) technology. This phase shifter (PS) is designed to obtain a phase shift of 90 degrees at the resonance frequency. In this design, the stub matching technique is employed to match the impedance. This design is tunable, thereby obtaining a phase shift from 45 to 90 degrees with a phase deviation of 5 degrees in the resonance frequency of 2.4GHz. A varactordiode is introduced to make the design tunable. The design is carried out using the FR4 substrate, with an operating frequency of 2.4GHz. An IE3D full-wave simulation platform is used for simulation purposes.
Chapter 21 explores manufacturing competencies and sustainability issues for automobile manufacturing companies. The work is based on organizations in north Indian automobile and auto parts manufacturing companies, with various manufacturers being treated alike irrespective of the manufacturing sector. A qualitative model was developed for depicting competency and strategy relation. The research provided an insight into manufacturing competencies and their relation tostrategic success; it also discovered areas that could be improved with further research.
Chapter 22 creates a nonlinear continuous review inventory model for multiple products considering the quantity of the products received as uncertain with controllable lead time. The solution of the model was discussed with and without considering service level constraint. The Lagrangian method is applied for the model with service level constraints and an optimal solution is arrived at.
Part III: Metaheuristics – Applications and Innovations
Chapter 23 proposes a completely innovative metaheuristic