Knowledge Discovery Methods Research Articles

Knowledge Discovery of Hospital Medical Technology Based on Partial Ordered Structure Diagrams

So far, no research has used the partial order algorithm for the mining of hospital medical technology. This paper proposed a novel knowledge discovery method of hospital medical technology based on partial ordered structure diagrams, constructed attribute partial ordered structure diagram and object partial ordered structure diagram for the formal context constructed by hospital set and medical technology set, and finally analyzed them using the knowledge discovery method. The experiments show that the partial ordered structure diagram can effectively visualize the structural relationships between hospital sets and medical technology sets, and the distribution characteristics of medical technology sets in hospital sets and the rules of medical technology sets owned by hospital sets can be obtained based on the node, branch, and group structure relationships of the partial ordered structure diagram.

An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

A Big Data-Driven Intelligent Knowledge Discovery Method for Epidemic Spreading Paths

The prevention and control of communicable diseases such as COVID-19 has been a worldwide problem, especially in terms of mining towards latent spreading paths. Although some communication models have been proposed from the perspective of spreading mechanism, it remains hard to describe spreading mechanism anytime. Because real-world communication scenarios of disease spreading are always dynamic, which cannot be described by time-invariant model parameters, to remedy such gap, this paper explores the utilization of big data analysis into this area, so as to replace mechanism-driven methods with big data-driven methods. In modern society with high digital level, the increasingly growing amount of data in various fields also provide much convenience for this purpose. Therefore, this paper proposes an intelligent knowledge discovery method for critical spreading paths based on epidemic big data. For the major roadmap, a directional acyclic graph of epidemic spread was constructed with each province and city in mainland China as nodes, all features of the same node are dimension-reduced, and a composite score is evaluated for each city per day by processing the features after principal component analysis. Then, the typical machine learning model named XGBoost carries out processing of feature importance ranking to discriminate latent candidate spreading paths. Finally, the shortest path algorithm is used as the basis to find the critical path of epidemic spreading between two nodes. Besides, some simulative experiments are implemented with use of realistic social network data.

Role of the antineoplastic drug bleomycin based on toxicogenomic-DNA damage inducing (TGx-DDI) genomic biomarkers data: A meta-analysis.

Accurately identifying the cellular, biomolecular, and toxicological functions of anticancer drugs help to decipher the potential risk of genotoxicity and other side effects. Here, we examined bleomycin for cellular, molecular and toxicological mechanisms using next-generation knowledge discovery (NGKD) tools. This study was conducted at the Faculty of Applied Medical Sciences, King Abdulaziz University (KAU), Jeddah, Saudi Arabia in October 2022. We first analyzed the raw Toxicogenomic and DNA damage-inducing (TGx-DDI) gene expression data from Gene Expression Omnibus (GEO) (GSE196373) of TK6 cells treated with 10 µM bleomycin and TK6 cells treated with DMSO for four hours using the GEO2R tool based on the Linear Models for Microarray Analysis (limma) R packages to derive the differentially expressed genes (DEGs). Then, iPathwayGuide was used to determine differentially regulated signaling pathways, biological processes, cellular, molecular functions and upstream regulators (genes and miRNAs). Bleomycin differently regulates the p53 pathway, transcriptional dysregulation in cancer, FOXO pathway, viral carcinogenesis, and cancer pathways. The biological processes such as p53 class mediator signaling, intrinsic apoptotic signaling, DNA damage response, and DNA damage-induced intrinsic apoptotic signaling and molecular functions like ubiquitin protein transferase and p53 binding were differentially regulated by bleomycin. iPathwayGuide analysis showed that the p53 and its regulatory gene and microRNA networks induced by bleomycin. Analysis of TGx-DDI data of bleomycin using NGKD tools provided information about toxicogenomics and other mechanisms. Integration of all "omics" based approaches is crucial for the development of translatable biomarkers for evaluating anticancer drugs for safety and efficacy.

Analysis of the Security and Reliability of Cryptocurrency Systems Using Knowledge Discovery and Machine Learning Methods

Cryptocurrency, often known as virtual or digital currency, is a safe platform and a key component of the blockchain that has recently attracted much interest. Utilizing blockchain technology, bitcoin transactions are recorded in blocks that provide detailed information on all financial transactions. Artificial intelligence (AI) has significant applicability in several industries because of the abundance and processing capacity of large data. One of the main issues is the absence of explanations for AI algorithms in the current decision-making standards. For instance, there is no deep-learning-based reasoning or control for the system's input or output processes. More particularly, the bias for adversarial attacks on the process interface and learning characterizes existing AI systems. This study suggests an AI-based trustworthy architecture that uses decentralized blockchain characteristics such as smart contracts and trust oracles. The decentralized consensuses of AI predictors are also decided by this system using AI, enabling secure cryptocurrency transactions, and utilizing the blockchain technology and transactional network analysis. By utilizing AI for a thorough examination of a network, this system's primary objective is to improve the performance of the bitcoin network in terms of transactions and security. In comparison to other state-of-the-art systems, the results demonstrate that the proposed system can achieve very accurate output.

Decoding the Impact of Genetic Variants in Gastric Cancer Patients Based on High-Dimensional Copy Number Variation Data Using Next-Generation Knowledge Discovery Methods

Objectives: Despite a reduction in the incidence and mortality rates of gastric cancer (GC), it remains the fifth most frequently diagnosed malignancy globally. A better understanding of the regulatory mechanisms involved in the progression and development of GC is important for developing novel targeted approaches for treatment. We aimed to identify a set of differentially regulated pathways and cellular, molecular, and physiological system development and functions in GC patients infected with H. pylori infection based on copy number variation (CNV) data using next-generation knowledge discovery (NGKD) methods. Methods: In this study, we used our previous CNV data derived from tissue samples from GC patients (n = 33) and normal gastric samples (n = 15) by the comparative genome hybridization (CGH) method using Illumina HumanOmni1-Quad v.1.0 BeadChip (Zenodo Accession No: 1346283). The variant effects analysis of genetic gain or loss of function in GC was conducted using Ingenuity Pathway Analysis (IPA) software. In addition, in silico validation was performed with iPathwayGuide software using high-throughput RNA sequencing (RNAseq) data (GSE83088) from GC patients. Results: We observed 213 unique CNVs in the control group, 420 unique CNVs in the GC group, and 225 common variants. We found that cancer, gastrointestinal diseases, and organismal injury and abnormalities were the three diseases or disorders that were most affected in the GC group. We also identified that the programmed cell death ligand 1 (PD-L1) cancer immunotherapy pathway, T-cell apoptosis, T-cell exhaustion, and Type 1 regulatory T-cell (Tr1 cells) specialization were dysregulated in GC patients. RNAseq data from GC patients showed that the PD-1/PD-L1 pathway was significantly upregulated in GC samples compared with controls. Conclusions: In conclusion, in the present study, we decoded differentially impacted GC-specific diseases and biological functions and pathways based on CNV data using NGKD methods that can be adopted to design personalized therapeutic approaches for patients with GC in a typical clinical milieu.

Load shedding for frequency and voltage control in the multimachine system using a heuristic knowledge discovery method

The voltage profile of different buses and the rotor dynamics of generators are adversely affected by a generator outage. Generator outages can be minimized using a variety of strategies and algorithms. An AI-based knowledge discovery approach has been reported in this article. This article proposes a technique for identifying sensitive loads and the amount of active and reactive power curtailment for rotor speed regulation and voltage management at the terminals. The MATLAB®/Simulink environment verifies and tests the method’s practicality on an IEEE-10-machine-39-bus system. Active power shedding is considered for rotor angle stability, while reactive power is also shedded for maintaining the terminal voltage at the loads. A sequential outage is considered to simulate a scenario where the two generators with the highest active and reactive power are taken out of service. The generator’s rotor speed, terminal voltage, and load are measured with and without load restriction. In all situations, the rotor and center of inertia speed are 1 p.u. The average steady-state load terminal voltage is 0.967 V. The average terminal voltage of all load buses improves from 0.933 to 0.972 and 0.936 to 0.971, case-wise. The reported results confirm and validate the effectiveness and applicability of the proposed technique.

Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods

Background:Coronavirus (CoV) disease (COVID-19) identified in Wuhan, China, in 2019, is mainly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and is caused by SARS CoV-2, which belongs to the Coronaviridae family. Determining the underlying disease mechanisms is central to the identification and development of COVID-19-specific drugs for effective treatment and prevention of human-to-human transmission, disease complications, and deaths.Methods:Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO) (GSE147507), and quality control (QC) was assessed before RNA Seq analysis using CLC Genomics Workbench 20.0. Differentially expressed genes (DEGs) were imported into BioJupies to decipher COVID-19 induced signaling pathways and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID -19 specific gene signatures. In addition, iPathwayGuide was used to identify COVID-19-specific signaling pathways, as well as drugs and natural products with anti-COVID-19 potential.Results:Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3). COVID-19 infection activated key infectious disease-specific immune-related signaling pathways such as influenza A, viral protein interaction with cytokine and cytokine receptors, measles, Epstein-Barr virus infection, and IL-17 signaling pathway. Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy.Conclusions:In conclusion, we have used the in silico next-generation knowledge discovery (NGKD) methods to discover COVID-19-associated pathways and specific therapeutics that have the potential to ameliorate the disease pathologies associated with COVID-19.

Is Big Data That Simple? An Analysis of the Transfer Pricing Aspects of Value Creation of Data in the Digitalized Economy

Data steers the digitalized economy. The transformation of data into valuable information is somewhat comparable with traditional research and development activities and has become one of the most valuable intangibles for many companies. The knowledge discovery method in databases, known as “data mining”, needs to be studied conceptually to demonstrate that the arm’s length principle cannot effectively allocate profits to jurisdictions. Nevertheless, much effort has been put into OECD’s Pillar One, but is it indeed ready to deal with the big data economy? To answer this question, the author briefly introduces the evolution of transfer pricing and presents that the dichotomy between the arm’s length principle comparability completeness and the formulaic apportionment simplicity were merged to create a more robust system to tackle the challenges of the digitalization and lack of physical presence. In a data-driven economy, it is essential to understand the steps involved in the value creation of data. The purpose of the present study is to meticulously delineate the value chain of big data, including collection, processing and analysis and monetization of the data. In conclusion, the author suggests a framework that effectively allocates profits in accordance with the value creation of data.

Knowledge Discovery in Case Studies: The Case Insight Method for Case-Based Problem Solving

The topic of this paper is a new method of knowledge discovery in documents called “Case Insight” (abbreviated to CI). The research question that led to this development was “How can we discover knowledge through case studies and make it usable for case-based problem solving?” To answer this question, this research took a Systems Thinking and Networked Thinking qualitative approach. Case-based problem-solving uses knowledge contained in authentic case descriptions (i.e. “good practice” or even “best practice” cases) and adapts it to the requirements of a new problem. Who can use this? Managers and management consultants who are starting out in their careers can benefit in particular from the CI method as it allows them to expand their repertoire of experience in problem-solving on the basis of case studies, i.e. without being involved in projects. All those interested in solving complex management problems in a case-based way also form part of the target audience. Case studies contain a great deal of problem-solving knowledge but only part of that knowledge can be absorbed through simple reading. The rest remains difficult to access, a hidden treasure, so to speak. Why is that? The reason is that knowledge discovery in case studies is made more difficult due to two obstacles: firstly, the texts are not sufficiently brain-friendly and secondly, they are not designed holistically enough. The CI method makes it possible to overcome these obstacles by means of CI tools and CI models. Firstly, CI tools are used to analyse case studies by comparing concepts, ideas, etc. and combining them into a whole; secondly, CI models make knowledge discovered in this way usable in the form of brain-friendly and holistic knowledge structures. Thus, knowledge discovery through the CI method complies with Immanuel Kant’s definition of knowledge as "a whole of compared and linked ideas".

Base on the Public Scientific Quality Improvement Research on Risk Early Warning of Online Shopping

In order to improve the scientific quality of the public, the Chinese Association for Science and Technology has put forward a call to combine popular science education with leisure and entertainment. In view of the fact that online shopping involves a wide range of areas, and the people pay more attention to it, the paper completed the innovation of online shopping risk warning science knowledge, the design of popular science mechanism and the dissemination of popular science knowledge. The paper used complex network’s knowledge discovery methods and decision theory to design online shopping risk warning science knowledge; Using the complex network public opinion dissemination trust analysis realize the dissemination of popular science knowledge and promote the improvement of the public's quality of popular science. The spread of risk early warning science knowledge in the network shows that the risk early warning mechanism designed can achieve the purpose of improving public science knowledge when the reward and punishment measures are appropriate.

A Neural Network Approach for Chinese Sports Tourism Demand Based on Knowledge Discovery

With the vigorous development of the Chinese economy and people's pursuit of quality, sports activities of people pursuit are no longer limited to simple physical exercise, but a way that pursues higher-quality sports tourism. As a new industry, it cannot guarantee that sports tourism will be accepted by all people, and it will be limited by geographical, economic, time, and other conditions. The participation number of Chinese sports tourism is more concerned by organizers or operators. Predicting the participation number of sports tourism based on the knowledge discovery method is meaningful and economical work. In this paper, a variety of sports tourism data are classified by clustering method, and the categories with similar characteristics are classified. Then, the convolution and long short-term memory hybrid neural network are used to extract the spatial and temporal information of sports tourism characteristics, which completes the prediction of Chinese sports tourism categories. The research results show that the clustering method has high accuracy for the classification of sports tourism categories, and the weights occupied by the categories are relatively uniform. The ConvLSTM neural network also has obvious advantages in predicting Chinese sports tourism methods. The largest error is only 2.89%, and the correlation coefficient also reaches 0.98, which is enough to be trusted for the prediction of Chinese sports tourism categories.

A Knowledge Discovery Method for Landslide Monitoring Based on K-Core Decomposition and the Louvain Algorithm

Landslide monitoring plays an important role in predicting, forecasting and preventing landslides. Quantitative explorations at the subject level and fine-scale knowledge in landslide monitoring research can be used to provide information and references for landslide monitoring status analysis and disaster management. In the context of the large amount of network information, it is difficult to clearly determine and display the domain topic hierarchy and knowledge structure. This paper proposes a landslide monitoring knowledge discovery method that combines K-core decomposition and Louvain algorithms. In this method, author keywords are used as nodes to construct a weighted co-occurrence network, and a pruning standard value is defined as K. The K-core approach is used to decompose the network into subgraphs. Combined with the unsupervised Louvain algorithm, subgraphs are divided into different topic communities by setting a modularity change threshold, which is used to establish a topic hierarchy and identify fine-scale knowledge related to landslide monitoring. Based on the Web of Science, a comparative experiment involving the above method and a high-frequency keyword subgraph method for landslide monitoring knowledge discovery is performed. The results show that the run time of the proposed method is significantly less than that of the traditional method.

Optimization of Human Resource Performance Management System Based on Improved R‐Means Clustering Algorithm

With the rapid development of network technology and database technology, computers have been able to store large‐scale and massive data. On the other hand, traditional data analysis and processing tools such as management information system can only process these data on the surface, but the deeper data analysis ability is not satisfactory. The contradiction between data supply ability and data analysis ability is becoming more and more prominent, so there is an urgent need for an automation technology that can deeply process data. Data mining technology came into being. Cluster analysis, as an important topic in data mining, is a data mining method that divides data into natural groups and gives the description of the characteristics of each group. It is a basic method of data mining and knowledge discovery. Cluster analysis is a data mining technology for unsupervised classification of data without prior knowledge and guidance. Through the appropriate use of advanced algorithms, it can explore the hidden valuable information, improve the quality of data analysis and interpretation, and provide a scientific judgment basis for the reprocessing or understanding of data by other data analysis and sorting tools. First, this paper briefly introduces the principle, development, and methods of cluster analysis and expounds the application of cluster analysis. Then it expounds the principle of R‐means clustering algorithm, analyzes the advantages and disadvantages of basic R‐means clustering algorithm, and expounds several existing improvement methods. An improved R‐means clustering algorithm and a clustering analysis model based on R‐means clustering algorithm are proposed, and the corresponding algorithm flow and implementation are given.

A Knowledge Discovery Method of Product Design Requirements Based on Pattern Matching

In conventional product design process, the designers firstly formulate a general design assignment according to the users’ demands, design experience, product running environment and so on, then professional designers from different departments carry out detailed module designs according to those design requirements in the assignment. This kind of design method is suitable for small-batch and single-variety production. However, with the emergence of mass customization production, reading lengthy design assignments has gradually become a difficulty that restricts the production efficiency for enterprises, as a result of which extracting the requirement knowledge in the design assignment efficiently and well-targeted is becoming more and more important. In this paper an expert system (ES) framework for product design requirements knowledge discovery is proposed Firstly, a knowledge base is established by analyzing the types of requirements. Secondly, the requirements extraction program of the reasoning program is proposed based on the information extraction method of regular expression and named entity library, combined with manual-assisted positioning. Thirdly, the requirement similarity evaluating method of reasoning program is established combined with the characteristic of demand attributes. Finally, the design assignment books of screw conveyor are taken as an example to evaluate the proposed method and it turns out 90% of coverage and precision rate within 50 design assignment books.

Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network

The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model’s explainability while learning from streaming spatiotemporal brain data (STBD) in an incremental and on-line mode of operation. This led to the extraction of spatiotemporal rules from SNN models that explain why a certain decision (output prediction) was made by the model. During the learning process, the SNN created dynamic neural clusters, captured as polygons, which evolved in time and continuously changed their size and shape. The dynamic patterns of the clusters were quantitatively analyzed to identify the important STBD features that correspond to the most activated brain regions. We studied the trend of dynamically created clusters and their spike-driven events that occur together in specific space and time. The research contributes to: (1) enhanced interpretability of SNN learning behavior through dynamic neural clustering; (2) feature selection and enhanced accuracy of classification; (3) spatiotemporal rules to support model explainability; and (4) a better understanding of the dynamics in STBD in terms of feature interaction. The clustering method was applied to a case study of Electroencephalogram (EEG) data, recorded from a healthy control group (n = 21) and opiate use (n = 18) subjects while they were performing a cognitive task. The SNN models of EEG demonstrated different trends of dynamic clusters across the groups. This suggested to select a group of marker EEG features and resulted in an improved accuracy of EEG classification to 92%, when compared with all-feature classification. During learning of EEG data, the areas of neurons in the SNN model that form adjacent clusters (corresponding to neighboring EEG channels) were detected as fuzzy boundaries that explain overlapping activity of brain regions for each group of subjects.

AB0386 CORONAVIRUS INFECTION AND VASCULITIS: IDENTIFYING ASSOCIATIONS MINING THE BIOMEDICAL LITERATURE

Background:Based on recent publications suggesting an association between COVID-19 and vascularInflammation.Objectives:Our aim was to explore new associations between coronavirus infections and vasculitis utilizing semantic mining of PubMed results.Methods:The following literature search string: “(vasculitis OR vascular inflammation OR vascular damage) AND (coronavirus OR SARS virus OR MERS-CoV OR Covid-19)” was used to retrieve abstracts from the whole PubMed database, using Semantic MEDLINE 2. on 6/7/2020. This application represents a network of semantic predications (triples of the form subject-predicate-object, e.g. COVID-19 causes Disease) on a knowledge graph. The system allows for choosing the maximum number of nodes represented, the central topic, and the length of the network. For our network we chose to display all relations, COVID-19 (31 edges) as the central term, 3 lengths, and selecting the most informative nodes. Automatic summarization eliminated the less informative predications.Results:The search string retrieved 152 citations from PubMed and identified 1,028 predications. Thenetwork (Figure 1), displayed using COVID-19 as the central term, consisted of 72 nodes and 140 edges. The 5 most connected nodes were ’Patients: 19 nodes’, COVID-19: 13’, ‘Inflammation: 13’, ’Lung: 11’, and ‘Disease: 11’. Multiple links have been found between coronavirus and vasculitis. Animal coronaviruses, including the one causing feline infectious peritonitis (FIP), the murine coronavirus mouse hepatitis virus (MHV), the SARS-CoV in transgenic mice and coronavirus in ferrets, are known to cause vasculitis in animals. It is known that coronaviruses that infect animals can evolve and become new human coronaviruses. SARS produces inflammation in blood vessels. In 2005, a link between the coronavirus HCoV-NL63 or New Haven Coronavirus (HCoV-NH) and KD was reported,although later studies concluded that HCoV-NH did not play a dominant role in the etiology orpathogenesis of KD. In 2014, serological testing suggested the possible involvement of CoV-229E in the development of KD. There has also been a report of KD patients being infected by coronavirus OC43/HKU1.COVID-19 may infect the vessels and trigger inflammatory reactions like those of vasculitis, including vasculitis-like cutaneous lesions. COVID-19 patients develop thrombosis, and increased risk of thrombosis is also present in primary vasculitic syndromes. Children, many of whom tested positive for COVID-19 antibodies, developed Multisystem Inflammatory Syndrome in Children (MIS-C), an inflammatory condition similar to Kawasaki Disease (KD).Conclusion:Knowledge integration and discovery methods are an efficient and powerful way of retrieving and analyzing relevan information from multiple papers. Their main advantages are finding relations among biomedical concepts, generating new hypotheses, and opening them to literature-based discovery.SARS-CoV-2 may cause vasculitis or vasculitis-like syndromes. The KD-like syndrome reported mainly in children with COVID-19 revives the previous suspicion of coronavirus as a possible triggering agent of KD and the decades-old hypothesis of infection involvement in the pathogenesis of vasculitis.

Using data mining techniques to fight and control epidemics: A scoping review.

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge. Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.

Detection and interpretation of erroneous data in statistical analysis of consumption of energy resources

Monitoring and analysis of consumption of energy resources in various contexts, as well as measuring of parameters (indicators) in time are of utmost importance for the modern economy. This work is dedicated to examination and interpretation of the anomalies of collecting data on consumption of energy resources (on the example of gas consumption) in the municipal formation. Gas consumption is important for the socioeconomic sphere of cities. Unauthorized connections are the key reason for non-technological waste of the resource. The traditional methods of detection of stealing of gas are ineffective and time-consuming. The modern technologies of data analysis would allow detecting and interpreting the anomalies of consumption, as well as forming the lists for checking the objects for unauthorized connections. The author’s special contribution lies in application of the set of statistical methods aimed at processing and identification of anomalies in energy consumption of a municipal formation. It is worth noting that the use of such technologies requires the development of effective algorithms and implementation of automation and machine learning algorithms. The new perspective upon time-series data facilitates identification of anomalies, optimization of decision-making, etc. These processes can be automated. The presented methodology tested on time-series data that describes the consumption of gas can be used for a broader range of tasks. The research can be combined with the methods of knowledge discovery and deep learning algorithms.

GC and Other Methods for Full and Partial Context Coverage

Formal Concept Analysis (FCA) is a popular method for knowledge discovery and data mining in binary data. A shortcoming of FCA is the huge number of formal concepts that may be drawn from formal contexts (binary data tables) of moderate size or larger. A strategy to deal with this shortcoming is to extract a subset of formal concepts that cover the context either fully or partially. We compare different methods for generating full and partial concept cover, present a simple Greedy Coverage (GC) method, and show that it is an efficient option, especially for generating partial concept cover.