The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia.    The publications within "Advances in Intelligent Systems and Computing" are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results.   Indexed by DBLP, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago.  All books published in the series are submitted for consideration in Web of Science.

Advances in Intelligent Systems and Computing

Blockchain technology can be extensively applied in diverse services, including online micro-payments, supply chain tracking, digital forensics, health-care record sharing, and insurance payments. Extending the technology to the Internet of things (IoT), we can obtain a verifiable and traceable IoT network. Emerging research in IoT applications exploits blockchain technology to record transaction data, optimize current system performance, or construct next-generation systems, which can provide additional security, automatic transaction management, decentralized platforms, offline-to-online data verification, and so on. In this article, we conduct a systematic survey of the key components of IoT blockchain and examine a number of popular blockchain applications. In particular, we first give an architecture overview of popular IoT-blockchain systems by analyzing their network structures and protocols. Then, we discuss variant consensus protocols for IoT blockchains, and make comparisons among different consensus algorithms. Finally, we analyze the traffic model for P2P and blockchain systems and provide several metrics. We also provide a suitable traffic model for IoT-blockchain systems to illustrate network traffic distribution.

A Survey of IoT Applications in Blockchain Systems: Architecture, Consensus, and Traffic Modeling

In this paper, the concept of information modeling based on a new model of geometric transformations is considered. This concept ensures the solutions of the following tasks like pattern recognition, predicting, classification, the principal independent components selection, optimization, recovering of lost data or their consolidation and implementing the information protection and privacy methods. Neural-like structures based on the Geometric Transformations Model as universal approximator implement principles of training and self-training and base on an algorithmic or hardware performing variants using the space and time parallelization principles. Geometric Transformations Model uses a single methodological framework for various tasks and fast non-iterative study with pre-defined number of computation steps, provides repeatability of the training outcomes and the possibility to obtain satisfactory solutions for large and small training samples.

Model and Principles for the Implementation of Neural-Like Structures Based on Geometric Data Transformations

The Internet of Things (IoT) is playing a vital role in the rapid automation of the healthcare sector. The branch of IoT dedicated towards medical science is at times termed as Healthcare Internet of Things (H-IoT). The key elements of all H-IoT applications are data gathering and processing. Due to the large amount of data involved in healthcare, and the enormous value that accurate predictions hold, the integration of machine learning (ML) algorithms into H-IoT is imperative. This paper aims to serve both as a compilation as well as a review of the various state of the art applications of ML algorithms currently being integrated with H-IoT. Some of the most widely used ML algorithms have been briefly introduced and their use in various H-IoT applications has been analyzed in terms of their advantages, scope, and possible improvements. Applications have been divided into the domains of diagnosis, prognosis and spread control, assistive systems, monitoring, and logistics. In healthcare, practical use of a model requires it to be highly accurate and to have ample measures against security attacks. The applications of ML algorithms in H-IoT discussed in this paper have shown experimental evidence of accuracy and practical usability. The constraints and drawbacks of each of these applications have also been described.

A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications

With the growth of online shopping, Credit Card Fraud (CCF) comes out as a serious menace. For this end, the automatic and real-time fraud detection field calls for several studies. The recent ones use many Machine Learning (ML) methods and techniques due to their beneficial characteristics to build a good fitting model to catch fraudulent transactions. The purpose of this paper is to develop a new model based on a hybrid approach for Credit Card Fraud Detection (CCFD). The proposed model, compared to previous studies, shows its strong ability to identify fraud transactions. Precisely, the robustness of our model is built by combining the strength of three sub-methods; the Recursive Feature Elimination (RFE) for selecting the most useful predictive features, the GridSearchCV for Hyper-Parameters Optimization (HPO) and the Synthetic Minority Oversampling (SMOTE) to overcome the imbalanced data problem. The experimentations of our model, on many real-world data sets, gives the best results in terms of efficiency and effectiveness.

Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization

The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.

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An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data Based on the GRNN-SGTM Ensemble.

The paper describes a new non-iterative linear supervised learning predictor. It is based on the use of Ito decomposition and the neural-like structure of the successive geometric transformations model (SGTM). Ito decomposition (Kolmogorov–Gabor polynomial) is used to extend the inputs of the SGTM neural-like structure. This provides high approximation properties for solving various tasks. The search for the coefficients of this polynomial is carried out using the fast, non-iterative training algorithm of the SGTM linear neural-like structure. The developed method provides high speed and increased generalization properties. The simulation of the developed method’s work for solving the medical insurance costs prediction task showed a significant increase in accuracy compared with existing methods (common SGTM neural-like structure, multilayer perceptron, Support Vector Machine, adaptive boosting, linear regression). Given the above, the developed method can be used to process large amounts of data from a variety of industries (medicine, materials science, economics, etc.) to improve the accuracy and speed of their processing.

https://www.mdpi.com/2306-5729/3/4/46/pdf

Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs

In this chapter, it is proposed the solutions of a problem of changing image resolution based on the use of computational intelligence means, which are constructed using the new neuro-paradigm—Geometric Transformations Model. The topologies, the training algorithms, and the usage of neural-like structures of Geometric Transformations Model are described. Two methods of solving a problem of reducing and increasing image resolution are considered: using neural-like structures of Geometric Transformations Model and on the basis of the matrix operator of the weight coefficients of synaptic connections. The influences of the parameters of image preprocessing procedure, as well as the parameters of the neural-like structures of Geometric Transformations Model on the work quality of both methods are investigated. A number of the practical experiments using different quality indicators of synthesized images (PSNR, SSIM, UIQ, MSE) are performed. A comparison of the effectiveness of the developed method with the effectiveness of the existing one is implemented.

Learning-Based Image Scaling Using Neural-Like Structure of Geometric Transformation Paradigm

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Roman Tkachenko

Papers

Model and Principles for the Implementation of Neural-Like Structures Based on Geometric Data Transformations

An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data Based on the GRNN-SGTM Ensemble.

Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs

Learning-Based Image Scaling Using Neural-Like Structure of Geometric Transformation Paradigm

The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production