Classification Issues Research Articles

Эссенциальный тремор: современный взгляд на проблему и новые возможности нейрохирургических методов лечения

The review is focused on essential tremor (ET), the most common extrapyramidal system disorder. Current understanding of the disease pathogenesis is provided; issues of classification and differential diagnosis are discussed. Modern ET treatment methods include therapeutic approaches and surgical interventions. The benefits of the new ET treatment method, the magnetic resonance-guided focused ultrasound treatment (MRgFUS), are described; the world’s experience of using the method, indications and contraindications are summarized.

Some issues of classification and etiopathogenesis of chronic colonic stasis

This paper presents a review of the literature, which covers the main issues of the etiology and pathogenesis of chronic colonic stasis, starting from the era of Avicenna to modern views. Various classifications are provided, covering all the nuances of this polyetiological problem, and risk factors that contribute to the development of such a severe pathology.

Breast Cancer Image Classification based on Adaptive Interpolation Approach Using Clinical Dataset

In the healthcare and bioinformatics disciplines, the categorization of breast cancer has become an emerging paradigm due to the second most common cause of cancer-related mortality in women. A biopsy is a procedure in which tissue has been examined to determine whether or not it is breast cancer through histopathologists that may lead to a mistaken diagnosis. The research is mainly focused on the patient data (884 case reports of patients) is acquired from the American Oncology Institute implemented with preprocessing techniques consists of missing values and those values are recovered with Novel Modified Interpolation (MI) Method. Deep learning networks effectively detect and assess the pattern for annotating histological data based on the labelling, which preserves time, system cost and enhances the system accuracy. This framework addressed feature acquisition and missing analysis strategies based on entropy confidence weight factor. First, the iterative patterns have been treated as a potential diagnostic rule, and the attention-based rule combination formulates the classification issue based on integrating convolutional and recurrent neural networks, and the short-term and long-term spatial correlations between patches. Second, the key part of label construction is carried out with an entropy confidence-weight factor assessment which detects and predict different patterns to construct the rule for classification. Third, optimization of clustering data by assessing missing parameters based on mean square error and the concept of interpolation to reduce data loss by around 20% and enhance the system accuracy. Simulation results show that the proposed system achieves 91.3% accuracy to state-of-art approaches, potentially allied in clinical applications. Modified Interpolation (MI) method recovered missing values with least mean square error and less data loss of 0.0123 and 1.38% respectively. This method also compared with existing Linear Interpolation (LI) method which is able to recover least men square error and data loss of 3.295 and 18.925% respectively. Comparatively the modified interpolation method recovered the missing values with less mean square error and less data loss.

A Novel Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

This work proposes a novel approach to object recognition, particularly for human faces, based on the principle of human cognition. The suggested approach can handle a dataset or problem with a large number of classes for classification more effectively. The model for the facial recognition-based object detection system was constructed using a combination of decision tree clustering based multi-level Backpropagation neural network classifier-TFMLBPNN-DTC and hybrid texture feature (ILMFD+GLCM) and applied on NS and ORL databases. This model produced the classification accuracy (±standard deviation) of 95.37 ±0.951877% and 90.83 ± 1.374369% for single input and 96.58 ±0.5604582% and 91.50 ± 2.850439% for group-based decision for NS and ORL database respectively. The better classification results encourage its application to other object recognition and classification issues. This work's basic idea also makes it easier to improve classification management for a wide range of classes.

تصنيف الجرائم الإلكترونية وفقاً لطبيعة الحق المعتدى عليه دراسة مقارنة بين التشريع الفلسطيني والإماراتي

This research investigates the issue of the different classifications of electronic crimes, and the importance of classifying them in the criminal law, with a focus on the specificity and the importance of the right assaulted in classifying them, as well as the importance of classifying them according to this criterion at all stages of the legal process to Combatting phenomenon of electronic crime. In this study we also investigates, the approach of Palestinian and Emirati legislators in classifying and regulating the provisions electronic crimes and Communications Crimes & information technology, pursuant to Decree-Law No. 10 of 2018, and Federal Law Decree-Law No. 34 of 2021 on combating phenomenon of electronic crime. At the end of the study, the researcher reached a set of conclusions and recommendations, One of the most important of conclusions is that The classification of electronic crimes based on the criterion of the right assaulted constitutes a vital issue in confronting these crimes, just as the Palestinian legislator did not follow a specific approach in his classification of electronic crimes and their substantive and procedural provisions, as the UAE legislator did in organizing and classifying crimes according to a clear approach. One of the most important recommendations is the necessity of the legislator the need for the Palestinian legislator to make amendments to the electronic crimes Law, classify these crimes, and separate the substantive and procedural provisions within this law, as well as amend the texts of Articles (21, 28, 48) of this law.

The significance and role of contra-asset accounts in accounting theory and practice

Subject. This article deals with the issues of classification of accounts by purpose and structure, as well as the application of contra-asset accounts. Objectives. The article aims to assess the significance and role of contra-asset accounts in the accounting system in its theoretical and practical aspects, as well as clarify the name of this classification group of accounts. Methods. For the study, we used the methods of information retrieval, generalization, modeling, analysis and synthesis. Results. Based on the study and generalization of theoretical provisions that reveal the issues and problems of the use of accounting accounts, the article presents proposals for clarifying the classification of contra-asset accounts and determines their theoretical and practical significance in the formation of information in the accounting system and financial reporting. Conclusions and Relevance. The use of the results of the study can help deeper understand the significance and role of contra-asset accounts and the multifunctional nature of information provided by their use in the formation of accounting (financial) statements and the solution of various management tasks for the management of processes related to the acquisition, use, storage, assessment of the state of objects recorded in the main accounts. This will create prerequisites for the competent use of this group of accounts in the practical activities of accountants. The materials of the study can be used to study and teach the theory of accounting, financial accounting, accounting (financial) reporting, as well as in the practical activities of specialists in the field of accounting.

On Mitigating the Utility-Loss in Differentially Private Learning: A New Perspective by a Geometrically Inspired Kernel Approach

Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning. This paper introduces a geometrically inspired kernel-based approach to mitigate the accuracy-loss issue in classification. In this approach, a representation of the affine hull of given data points is learned in Reproducing Kernel Hilbert Spaces (RKHS). This leads to a novel distance measure that hides privacy-sensitive information about individual data points and improves the privacy-utility tradeoff via significantly reducing the risk of membership inference attacks. The effectiveness of the approach is demonstrated through experiments on MNIST dataset, Freiburg groceries dataset, and a real biomedical dataset. It is verified that the approach remains computationally practical. The application of the approach to federated learning is considered and it is observed that the accuracy-loss due to data being distributed is either marginal or not significantly high.

Legal Vocabulary as a Means of Conceptualization of Cultural Communicative Situations by Aristocrats in Marcel Proust’s Novel

The author of the article argues that during the Third Republic French aristocrats used legal terms figuratively in their communication as is evidenced in Marcel Proust’s novel. The paper presents legal terms which, used metaphorically and in similes, create a certain cultural code for the personages’ private communication. Communicative situations (secular and political) employing figurative legal terms whose meaning can be perceived only by French aristocrats and their closest social circle, creating a special code for message exchange, served as material for our study. In order to describe these situations, Marcel Proust resorts to general legal vocabulary, the terms of Constitutional Law, Civil Law, International Public Law, as well as some concepts of the history of the French Law. The main methods of our study are semantic, linguistic, stylistic and contextual types of analysis. The relevance of our article could be justified by the fact that, to our knowledge, there are no scientific papers on stylistic devices, based on specialist terminology, in a literary text, or in communication. Linguists are interested in the functioning of specialist terminology as well as in the issues of classification and analysis of different stylistic devices. With regard to the stylistic analysis, we may say that the Guermants, the personages of the novel, are capable of impacting the opinion of their interlocutors in many subtle ways, just like journalists impact the viewpoints of their readers while covering political events in the news. For this purpose, they also resort to different delicate and ingenious techniques, such as play on words, aiming at belittling someone in the eyes of others. The inability of the readers to understand the meaning of some of their statements featuring legal terms to refer to some larger cultural and communicative situations, is one way for the Guermants to underline their own uniqueness, treating the people around them in terms of in-(out-)group members. Numerous communicative situations related to the figurative use of legal vocabulary allow the writer to represent Marcel’s disappointment by the aristocrats’ world he is now familiar with, as well as his misunderstanding of Baron de Charlus’ sensual hints, and total incomprehension of the new modern art by the Duke of Guermants.

К вопросу о теории деконцентрации власти

This article examines the issues of the theory of deconcentration in the domestic and foreign scientific literature. In this context, the author consistently examines the existing positions regarding the understanding of the category “deconcentration” and its relationship with the concept of “decentralization”, and also studies the types of deconcentration. In particular, it is noted that in the Russian legal system, unlike other countries, this term is not used. Thus, deconcentration as one of the factors of the organization of power has been reflected in France and Cambodia. However, despite the absence of mention in Russian legislation, this mechanism is widely used within the framework of the territorial organization of public administration. As for the literature, most researchers point out that deconcentration is one of the forms of decentralization of public power. At the same time, among other manifestations of the latter, it is characterized by the least degree of autonomy of local government structures. The author understands by deconcentration the movement of the central government to the periphery through the formation of territorial subjects (or the empowerment of local subjects) as agents (representatives) of the central government, characterized by relations of power and subordination and not involving the creation of a separate level of public authority. The article also touches upon the issue of classification of the types of the analyzed legal phenomenon.

Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model.

The oral microbial group typically represents the human body's highly complex microbial group ecosystem. Oral microorganisms take part in human diseases, including Oral cavity inflammation, mucosal disease, periodontal disease, tooth decay, and oral cancer. On the other hand, oral microbes can also cause endocrine disorders, digestive function, and nerve function disorders, such as diabetes, digestive system diseases, and Alzheimer's disease. It was noted that the proteins of oral microbes play significant roles in these serious diseases. Having a good knowledge of oral microbes can be helpful in analyzing the procession of related diseases. Moreover, the high-dimensional features and imbalanced data lead to the complexity of oral microbial issues, which can hardly be solved with traditional experimental methods. To deal with these challenges, we proposed a novel method, which is oral_voting_transfer, to deal with such classification issues in the field of oral microorganisms. Such a method employed three features to classify the five oral microorganisms, including Streptococcus mutans, Staphylococcus aureus, abiotrophy adjacent, bifidobacterial, and Capnocytophaga. Firstly, we utilized the highly effective model, which successfully classifies the organelle's proteins and transfers to deal with the oral microorganisms. And then, some classification methods can be treated as the local classifiers in this work. Finally, the results are voting from the transfer classifiers and the voting ones. The proposed method achieved the well performances in the five oral microorganisms. The oral_voting_transfer is a standalone tool, and all its source codes are publicly available at https://github.com/baowz12345/voting_transfer.

Genesis and retrospective analysis of the classification of persons sentenced to imprisonment in the Republic of Kazakhstan

Relevance. The relevance of the study is determined by the need to improve approaches to the classification of convicts and further humanisation of the conditions in detention facilities considering the historical experience of the implementation of penal enforcement legislation of different historical periods of Kazakhstan. Purpose. The purpose of the study is a scientific analysis of the processes of development of the legislation of the Republic of Kazakhstan in the field of classification of persons sentenced to imprisonment, which would allow prioritising the mechanism of distribution of convicts, and conclude that it is necessary to abandon formal classification in favour of a flexible approach in the implementation of an individual risk assessment of any particular convict. Methodology. The basis of the methodological approach in this study is a qualitative combination of general scientific and special methods of cognition of empirical and theoretical levels of research. Results. The results are the conclusions obtained in the process of analysing the legislation of different historical periods of Kazakhstan on the need to involve scientists, practicing psychologists, lawyers, and mediators in the classification of persons sentenced to imprisonment, which would contribute to the compilation of a complete, comprehensive description of the convict. The results of this study are of significant importance for researchers whose sphere of scientific interests is related to the issues of primary and secondary classification of persons sentenced to imprisonment, with the investigation of the legal status of convicts. Conclusions. The conclusions may be useful for subjects whose powers provide for the classification of convicts in practice, for employees of correctional facilities, lawyers, and prosecutors. The practical significance of this study is conditioned by the analysis of the historical development of the classification of convicts, which can ensure

Computational approach based on use of multilayer neural networks in classification of rail line states

The article considers the issue of classification of the states of rail lines using multilayer neural networks. It is shown that with significant external disturbing influences, the most promising direction is the retraining of classification models using neural networks. The representativeness of the sample is proposed to be realized by applying the scaling of informative features. It is proposed to minimize classification errors by gradient optimization methods. In the work during classification of rail lines states by neural networks, at the first stage, artificial classes of states are formed. At the second stage, the classification of states by the trained neural network is considered, classification errors are identified. On the basis of these errors the network is retrained until acceptable results are obtained. The article presents neuron learning algorithms: Hebb’s algorithm, error backpropagation method (for training a multilayer neural network), Konohen’s, Hopfield’s, Hamming’s algorithm and network. In the presented revision of the classification model, the correction is carried out according to the Widrow-Hoff algorithm.

Altruistic Collaborative Learning.

This article proposes a new learning paradigm based on the concept of concordant gradients for ensemble learning strategies. In this paradigm, learners update their weights if and only if the gradients of their cost functions are mutually concordant in a sense given by paper. The objective of the proposed concordant optimization framework is robustness against uncertainties by postponing to a later epoch, the consideration of examples associated with discordant directions during a training phase. Concordance constrained collaboration is shown to be relevant, especially in intricate classification issues where exclusive class labeling involves information bias due to correlated disturbances affecting almost all training examples. The first learning paradigm applies on a gradient descent strategy based on allied agents, subjected to concordance checking before moving forward in training epochs. The second learning paradigm is related to multivariate dense neural matrix fusion, where the fusion operator is itself a learnable neural operator. In addition to these paradigms, this article proposes a new categorical probability transform to enrich the existing collection and propose an alternative scenario for integrating penalized SoftMax information. Finally, this article assesses the relevance of the above contributions with respect to several deep learning frameworks and a collaborative classification involving dependent classes.

Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping Study

Hierarchical multi-label classification problems typically deal with datasets with many attributes and labels, which can negatively impact the classifier performance. The application of dimensionality reduction methods can significantly improve the performance of classifiers. Dimensionality reduction can be performed by feature extraction or feature selection, according to the problem domain and datasets characteristics. This work carried out a systematic literature mapping to identify the approaches and techniques of dimensionality reduction that have been used in hierarchical multi-label classification tasks. Searches were performed on 7 important databases for the Computer Science field. From a list of 184 retrieved papers, 12 were selected for analysis, from which it was possible to determine a general overview of studies conducted from 2010 to 2022. It was identified that feature selection was the most frequent reduction method, with filter approach standing out. In addition, it was detected that most of the works used tree hierarchical structure. As its main outcome, this paper presents the state of the art of dimensionality reduction problem for hierarchical multi-label classification, indicating trends and research issues in the field.

A taxonomic revision of the genus Angelica (Apiaceae) in Taiwan with a new species A. aliensis

BackgroundAngelica L. sensu lato is a taxonomically complex genus, and many studies have utilized morphological and molecular features to resolve its classification issues. In Taiwan, there are six taxa within Angelica, and their taxonomic treatments have been a subject of controversy. In this study, we conducted a comprehensive analysis incorporating morphological and molecular (cpDNA and nrDNA) characteristics to revise the taxonomic treatments of Angelica in Taiwan.ResultsAs a result of our research, we have revised the classification between A. dahurica var. formosana and A. pubescens and merged two varieties of A. morrisonicola into a single taxon. A new taxon, A. aliensis, has been identified and found to share a close relationship with A. tarokoensis. Based on the morphological and molecular characteristics data, it has been determined that the former three taxa should be grouped into the Eurasian Angelica clade, while the remaining four taxa should belong to the littoral Angelica clade. Furthermore, Angelica species in Taiwan distributed at higher altitudes displayed higher genetic diversity, implying that the central mountain range of Taiwan serves as a significant reservoir of plant biodiversity. Genetic drift, such as bottlenecks, has been identified as a potential factor leading to the fixation or reduction of genetic diversity of populations in most Angelica species. We provide key to taxa, synopsis, phenology, and distribution for each taxon of Taiwan.ConclusionsOur comprehensive analysis of morphological and molecular features has shed light on the taxonomic complexities within Angelica in Taiwan, resolving taxonomic issues and providing valuable insights into the phylogenetic relationships of Angelica in Taiwan.

Boosted stochastic fuzzy granular hypersurface classifier

In this work, we design a boosted stochastic fuzzy granular hypersurface classifier (BSFGHC) to resolve the classification issue of numerical data and non-numerical data (such as information granules) from the standpoint of granular computing. The scheme is divided into three parts: first, we present an adaptive cluster center clustering (ACCC) algorithm to achieve cluster centers of the data and to realize the fuzzy granulation of data parallelly based on Spark, which dramatically improves the granulation efficiency; second, we build a fuzzy granular space, design various fuzzy granular operators and measurement in the space to construct fuzzy granular hypersurfaces, create the loss function, and employ Particle Swarm Optimization (PSO) to resolve the optimal fuzzy granular hypersurfaces; third, we randomly divide the fuzzy granules to train multiple optimal fuzzy granular hypersurfaces and combine with the classification accuracy of fuzzy hypersurfaces and the difficulty of fuzzy granule subset classification to form a boosted fuzzy hypersurface to predict the data comprehensively. Experimental results and theoretical analysis demonstrate the outstanding performance of the method.

Knowledge Graph Enhanced Transformers for Diagnosis Generation of Chinese Medicine.

Chinese medicine (CM) diagnosis intellectualization is one of the hotspots in the research of CM modernization. The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues, however, it is difficult to solve the problems such as excessive or similar categories. With the development of natural language processing techniques, text generation technique has become increasingly mature. In this study, we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues. The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory (BILSTM) with Transformer as the backbone network. Meanwhile, the CM diagnosis generation model Knowledge Graph Enhanced Transformer (KGET) was established by introducing the knowledge in medical field to enhance the inferential capability. The KGET model was established based on 566 CM case texts, and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence (LSTM-seq2seq), Bidirectional and Auto-Regression Transformer (BART), and Chinese Pre-trained Unbalanced Transformer (CPT), so as to analyze the model manifestations. Finally, the ablation experiments were performed to explore the influence of the optimized part on the KGET model. The results of Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2 and Edit distance of KGET model were 45.85, 73.93, 54.59 and 7.12, respectively in this study. Compared with LSTM-seq2seq, BART and CPT models, the KGET model was higher in BLEU, ROUGE1 and ROUGE2 by 6.00-17.09, 1.65-9.39 and 0.51-17.62, respectively, and lower in Edit distance by 0.47-3.21. The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance. Additionally, the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results. In conclusion, text generation technology can be effectively applied to CM diagnostic modeling. It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models. CM diagnostic text generation technology has broad application prospects in the future.

On the Issue of Diagnosis and Treatment of Tunnel Neuropathy of the Ulnar Nerve in the Cubital Canal

The article presents current data on the problem of the renal canal syndrome, which is observed in people of working age in certain professions. Anatomical features of the canal and biomechanics of the elbow joint create prerequisites for compression of the ulnar nerve. The issues of distribution, classification, clinic, and effectiveness of various diagnostic methods are considered in detail. The treatment methods are analyzed in detail: conservative and surgical. If conservative treatment is ineffective, various options for surgical decompression are used. Special attention is paid to postoperative complications. Provocative tests, ultrasound, electro-neuromyography, and magnetic resonance imaging are used to diagnose the disease.

Improvement of classification and typification of operational conditions of wheeled vehicles in the Kyrgyz Republic

This article considers the issues of classification and typification of operating conditions of wheeled vehicles in the Kyrgyz Republic. The analysis of existing classification and typification of operating conditions by terrain and ambient air temperature is carried out. New information coding of administrative divisions of the Kyrgyz Republic with implementation of QR-coding has been introduced. And also the questions on improvement of the methodology of classification and typification of operational conditions of wheeled vehicles in the Kyrgyz Republic by the above-mentioned indicators are considered.

Classification issues and image problems in men’s folk dances

Classification issues and image problems in men’s folk dances