Handbook of Intelligent Computing and Optimization for Sustainable Development - Группа авторов

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solutions without the use of objective function and are less prone to local optima.

      This book provides the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including the IoT, manufacturing, optimization and healthcare. In general, the book is intended as a presentation of a wide range of publicly advanced achievements in the field of intelligent computing and optimization, which will be useful for a wide range of readers and will doubtless have a positive impact on a solution to the problem of sustainable development.

      Based on double-blind review processes, the 41 chapters were accepted for publication according to their suitability for the five parts of the book. A brief description of each chapter is given below.

       Part I: Intelligent Computing and Applications

      Chapter 1 proposes a mental workload prediction model using a neural network and the Bernoulli Boltzmann machine. For measuring mental workload, eye movement metrics were considered. The eye metrics were computed from raw eye movement data, which were recorded using an eye tracking device while solving coding problems. The authors found that the Bernoulli Boltzmann machine provides better accuracy in predicting mental workload from eye metrics.

      Chapter 2 discusses the development of an artificial neural network (ANN) using short DNA strands, i.e., oligonucleotides. The short sequences of DNA molecules can be used to code input and output signals and to build the basic architecture of the neuron. The authors also illustrate the design methodology of DNA logic gates and DNA logic circuits. Because of a few drawbacks, viz. immense energy consumption, vast memory requirement and heat dissipation, the traditional computation approaches the limitations of its processing power and design strategy. The aim of the authors is a paradigm shift in the computational world; from silicon to carbon. The design strategies discussed in this chapter are essential for effective development of a practical DNA computer.

      Chapter 3 focuses on a novel framework for detection of garments of interest from the footage of a surveillance camera. The video frames are processed using the GMG background subtraction model to obtain relevant foreground information along with foreground masks. The Mask R-CNN object detection model is used to identify customers and multiple image processing techniques are used to obtain the active garments in these frames. The detected customers are tracked and the OpenPose human pose estimation framework is utilized on them to obtain useful landmarks. The garments of interest are then determined based on the filtration of confidence scores calculated for each active garment. The framework was tested on a CCTV video dataset and was found to be effective despite facing arduous obstacles such as background noise and occlusions.

      Chapter 4 studies matrix algebra and elliptic curve arithmetic computing based on the integration of modular arithmetic and complex number arithmetic. It describes the intelligent computing of nonlinear transformations based on the residue matrices and the elliptic curve arithmetic over complex plane that can be applied in the computer science fields, which deal with cryptographic applications for more security. Their mathematical properties over complex plane are applied to create the cryptographic nonlinear transformation techniques in traditional ciphers, elliptic curve cryptography and quantum cryptography.

      Chapter 6 envisions how all present networks can be compiled into a single crowd associated network called a CrAN. This chapter contains a routing protocol for the proposed network. All applications for the proposed network are also discussed. The authors highlight the importance of creating this network and how it tackles transmission problems better than other networks. Some limitations of the proposed network are also mentioned.

      Chapter 7 demonstrates the application of a neural network (NN)-based group method of data handling (GMDH) for prediction of permeate flux (%) in disc-shaped membrane. The permeate flux is predicted using three parameters for this study such as pore size, operating pressure, and feed velocity. Different statistical techniques, such as mean absolute error, root mean square error (RMSE), RMSE-observation standard deviation error, and Pearson’s correlation coefficient, are analyzed in order to show the precision of the GMDH-NN models. To demonstrate the performance of the GMDH-NN model, the total error values are compared with the developed artificial neural network model. The study illustrates that the GMDH model predicts permeate flux of disc membrane with high accuracy.

      Chapter 8 introduces a new approach to identify nonfunctional needs by using nonfunctional requirements (NFR) catalogs via machine-learning methods, and proposes a process to acquire these catalogs by using a systemic mapping study of “lightweight.” The authors analysis provides a way of generating data sets used to classify non-functional specifications by NFR catalogs extracted from the mapping research in order to address the circumstance. They focus on the definition of the various NFR forms, with an emphasis on usability, security, performance and adaptability.

      Chapter 9 proposes an efficient and simple image recognition classification system, which consists of components from both reinforcement learning and deep learning. More specifically, Q-Learning is used with an agent having 2 states, and 2 to 3 actions. This classifier is different from others, because the latter use features of convolutional neural networks and also uses past histories in addition to Q states. Since the novel technique proposed has only 2 Q states, it has the advantage of being simple and also having significantly less parameters to optimize. The classifier given in this work performs better than other classifiers on the various datasets used experimentally.

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