Development Status and Multilevel Classification Strategy of Medical Robots

The combination of artificial intelligence technology and medical science has inspired the emergence of medical robots with novel functions that use new materials and have a neoteric appearance. However, the diversity of medical robots causes confusion regarding their classification. In this paper, we review the concepts pertinent to major classification methods and development status of medical robots. We survey the classification methods according to the appearance, function, and application of medical robots. The difficulties surrounding classification methods that arose are discussed, for example, (1) it is difficult to make a simple distinction among existing types of medical robots; (2) classification is important to provide sufficient applicability to the existing and upcoming medical robots; (3) future medical robots may destroy the stability of the classification framework. To solve these problems, we proposed an innovative multilevel classification strategy for medical robots. According to the main classification method, the medical robots were divided into four major categories—surgical, rehabilitation, medical assistant, and hospital service robots—and personalized classifications for each major category were proposed in secondary classifications. The technologies currently available or in development for surgical robots and rehabilitation robots are discussed with great emphasis. The technical preferences of surgical robots in the different departments and the rehabilitation robots in the variant application scenes are perceived, by which the necessity of further classification of the surgical robots and the rehabilitation robots is shown and the secondary classification strategy for surgical robots and rehabilitation robots is provided. Our results show that the distinctive features of surgical robots and rehabilitation robots can be highlighted and that the communication between professionals in the same and other fields can be improved.

Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining

In this paper, the multilevel classification model of high-speed railway signal equipment fault based on text mining technology is proposed for the data of high-speed railway signal fault. An improved feature representation method of TF-IDF is proposed to extract the feature of fault text data of signal equipment. In the multilevel classification model, the single-layer classification model was designed based on stacking integrated learning idea; the recurrent neural network BiGRU and BiLSTM were used as primary learners, and the weight combination calculation method was designed for secondary learners, and k-fold cross verification was used to train the stacking model. The multitask cooperative voting decision tree was designed to correct the membership relationship of classification results of each layer. Ten years of signal switch machine fault data of high-speed railway are used for experimental analysis; the experiment shows that the multilevel classification model can effectively improve the classification of signal equipment fault multilevel classification task evaluation index and can ensure the correctness of the subordinate relations’ classification results.

Accurate Multilevel Classification for Wildlife Images

The most common approaches for classification rely on the inference of a specific class. However, every category could be naturally organized within a taxonomic tree, from the most general concept to the specific element, and that is how human knowledge works. This representation avoids the necessity of learning roughly the same features for a range of very similar categories, and it is easier to understand and work with and provides a classification for each abstraction level. In this paper, we carry out an exhaustive study of different methods to perform multilevel classification applied to the task of classifying wild animals and plant species. Different convolutional backbones, data setups, and ensembling techniques are explored to find the model which provides the best performance. As our experimentation remarks, in order to achieve the best performance on the datasets that are arranged in a tree-like structure, the classifier must feature an EfficientNetB5 backbone with an input size of 300 × 300 px, followed by a multilevel classifier. In addition, a Multiscale Crop data augmentation process must be carried out. Finally, the accuracy of this setup is a 62% top-1 accuracy and 88% top-5 accuracy. The architecture could benefit for an accuracy boost if it is involved in an ensemble of cascade classifiers, but the computational demand is unbearable for any real application.

System of Chemical Bonds and Compounds as a Modern Scientific Foundation for Unification of Methods for Controlling the Structure and Properties of Various Substances and Materials

Modern scientific foundation for the unification of methods of control and analysis of structural features and practically important properties of various metallic and non-metallic polymeric, ceramic materials are considered. Within the framework of improving materials' control and analysis methods through taking into account the effects of chemical elemental composition on their structure and properties, a new fundamental approach was developed. This method, unlike others, is applicable to both metals and non-metals, and implies considering the impact of both the composition and the type of chemical bonding on structure and properties of materials. This was done on the basis of a unified multilevel classification of structure of metallic and non-metallic materials, the use of a unified model and system of chemical bonds and compounds, which allowed evaluating the effect of mixed types of chemical bonds on characteristics of their multilevel structure and properties.

Comprehensive сlassification of virtual assets

Background: One of the problems of the modern lawmakers in different countries is that they try to regulate an object before they study the nature of its origin, which, logically, entails many errors regarding its definition in the legal framework. The absence of unified definitions and clear classification of virtual assets as tools for implementing the methods of financial and management accounting of property according to their fundamental and unique features makes it nearly impossible to determine the features of virtual assets important for legal regulation and, therefore, to enshrine them in laws and establish a proper legal framework. The paper is dedicated to solving a relevant and cross-discipline scientific and applied task of developing a comprehensive multilevel classification of virtual assets. Unlike the few existing classifications that focus on parts of the virtual asset phenomenon and selective methods of its implementation, the paper proposes an all-encompassing comparison of all known types of virtual assets, which confirms the comprehensiveness of the classification proposed in this paper. Purpose: To develop and substantiate a comprehensive and multilevel classification of known types of virtual assets, which allows solving the cross-discipline scientific and applied task of systematizing virtual assets for future development of a single approach to regulating relations, the objects of which are different types of virtual assets. Materials and Methods: In order to study the nature of virtual assets and develop a comprehensive classification, a set of scientific research methods has been used: analysis, including cause and effect analysis, synthesis, comparison, generalization, systematization and interpretation of results and induction. Results: The author describes a triune nature of virtual assets: technological, economic and legal, information and applied. This classification of virtual assets will allow determining promising tools for accounting of property and rights. Unlike other known approaches to differentiating virtual assets, where crypto-assets (or cryptocurrencies) were unjustified “leaders”, the author has distinguished the group of tokenized assets for the first time. This particular group, due to its direct relation to property, allows performing accounting as well as reaccounting of property and rights in modern digital accounting systems – decentralized information platforms based on the distributed ledger technology (blockchain), whereas this accounting cannot be performed using crypto-assets due to absence of direct relation to property. Out of virtual assets, the author distinguishes a digital asset and analyzes the semantic features of the term “digital asset”. The digital asset is based on a unique information resource as the original asset and on the property of derivativeness from the real asset, which greatly differentiates it from other types of virtual assets. All of that allows considering it as an effective tool for implementing the methods of financial and management accounting of property. Thus, owners of digital assets can use the new way of accounting of their property and personal non-property rights. Based on the properties of a digital asset, the author distinguishes other types of virtual assets: polyasset and monoasset, with the relevant examples. The author provides the characteristics of their features and structural components while comparing them to the features of digital assets and giving clear and well-known financial and legal analogies regarding the implementation of mutual obligations between parties to a traditional deal. The paper also contains the first systematization of seven properties and parameters of a tokenized asset and, therefore, description of properties of three variations of a tokenized asset: monoasset, polyasset and digital asset. This allowed presenting the varieties of virtual assets as a three-level classification based on the complexity of the nature of virtual assets. The author’s classification distinguishes seven types of virtual assets and contains their description. Conclusions: Overall, the proposed approach to classification allows giving a scientific answer to the question of how to compare the multitude of known virtual assets and how to relate them to the legal framework of a state. These developments will be useful for legislators in basically every country, financial, tax and banking state bodies, as well as private companies when keeping books and performing accounting of virtual assets in their business activity.

Prediction With Mixed Effects Models: A Monte Carlo Simulation Study

Oftentimes in many fields of the social and natural sciences, data are obtained within a nested structure (e.g., students within schools). To effectively analyze data with such a structure, multilevel models are frequently employed. The present study utilizes a Monte Carlo simulation to compare several novel multilevel classification algorithms across several varied data conditions for the purpose of prediction. Among these models, the panel neural network and Bayesian generalized mixed effects model (multilevel Bayes) consistently yielded the highest prediction accuracy in test data across nearly all data conditions.

Deep Belief Network-Based Multifeature Fusion Music Classification Algorithm and Simulation

In this paper, the multifeature fusion music classification algorithm and its simulation results are studied by deep confidence networks, the multifeature fusion music database is established and preprocessed, and then features are extracted. The simulation is carried out using multifeature fusion music data. The multifeature fusion music preprocessing includes endpoint detection, framing, windowing, and pre-emphasis. In this paper, we extracted the rhythm features, sound quality features, and spectral features, including energy, cross-zero rate, fundamental frequency, harmonic noise ratio, and 12 statistical features, including maximum value, mean value, and linear slope. A total of 384-dimensional statistical features was extracted and compared with the classification ability of different emotional features. The deficiencies of the traditional classification algorithm are first studied, and then by introducing confusion, constructing multilevel classifiers, and tuning each level of the classifier, better recognition rates than traditional primary classification are obtained. This paper introduces label information for supervised training to further improve the features of multifunctional fusion music. Experiments show that this information has excellent performance in multifunctional fusion music recognition. The experiments compare the multilevel classifier with primary classification, and the multilevel classification with the primary classification and the classification performance is improved, and the recognition rate of the multilevel classification algorithm is also improved over the multilevel classification algorithm, proving that the excellent performance with multiple levels of classification.