Risk Identification System Research Articles

Unstructured Document Information Extraction Method with Multi-Faceted Domain Knowledge Graph Assistance for M2M Customs Risk Prevention and Screening Application

As a crucial national security defense line, the existing risk prevention and screening system of customs falls short in terms of intelligence and diversity for risk identification factors. Hence, the urgent issues to be addressed in the risk identification system include intelligent extraction technology for key information from Customs Unstructured Accompanying Documents (CUADs) and the reliability of the extraction results. In the customs scenario, OCR is employed for M2M interactions, but current models have difficulty adapting to diverse image qualities and complex customs document content. We propose a hybrid mutual learning knowledge distillation (HMLKD) method for optimizing a pre-trained OCR model’s performance against such challenges. Additionally, current models lack effective incorporation of domain-specific knowledge, resulting in insufficient text recognition accuracy for practical customs risk identification. We propose a customs domain knowledge graph (CDKG) developed using CUAD knowledge and propose an integrated CDKG post-OCR correction method (iCDKG-PostOCR) based on CDKG. The results on real data demonstrate that the accuracies improve for code text fields to 97.70%, for character type fields to 96.55%, and for numerical type fields to 96.00%, with a confidence rate exceeding 99% for each. Furthermore, the Customs Health Certificate Extraction System (CHCES) developed using the proposed method has been implemented and verified at Tianjin Customs in China, where it has showcased outstanding operational performance.

Governance Strategies and Philosophies for Combating Real Estate Money Laundering In Canada

In the realm of international criminal activities, public political figures (PEPs) frequently exploit the real estate market for money laundering purposes. The simplicity of this practice, coupled with substantial capital flows and stable returns, attracts criminal proceeds to Canada and other Western countries. Implementing anti-money laundering (AML) legislation and adhering to the recommendations of the Financial Action Task Force (FATF) have become pivotal in refining domestic AML systems and safeguarding international reputation. Studies of anti-money laundering directives from Europe, America, and the FATF, along with Canada's recent initiative in Vancouver to enhance transparency in land ownership, reveal that uncovering the identities of landowners and bolstering information sharing about high-risk clients are effective measures to prevent the infiltration of criminal funds into the real estate market. The application of this concept necessitates the establishment of a risk identification and management system based on big data as particularly essential.

IDENTIFICATION AS A STAGE OF RISK MANAGEMENT IN THE PROCESS OF CONFORMITY ASSESSMENT (CERTIFICATION) OF DEFENSE PRODUCTS

Certification of products, processes and services is a means of ensuring that they meet the requirements established in technical standards and other regulatory documents. Any activity in the field of conformity assessment (certification) of defense products is associated with the possibility of unforeseen events that may affect the achievement of results in accordance with the established goal, tasks, plans of the conformity assessment (certification) body. It must make decisions regarding the compliance of the evaluated defense products with regulatory requirements, which does not exclude the occurrence of certain risks that must be identified for risk optimization. The lack of established risk identification system is one of the important factors that can negatively affect the results of the Authority’s activities.
 The purpose of the article is to clarify the essence of the concept of risk identification in the field of conformity assessment (certification) of defense products as part of the overall risk assessment process, analysis of potential risk areas in the Authority’s activities, determination of identification criteria and approaches to its implementation. During the research, general scientific and special research methods were applied, including methods of system analysis and synthesis, comparative legal method, etc. An analysis of general and special risks that may arise in the field of conformity assessment (certification) has been carried out, the importance of identifying such risks has been determined; it was established that to improve the risk identification process, a comprehensive approach of the entire staff of the conformity assessment (certification) body is necessary.
 There is a justified need to create a risk identification system, which would include risk identification procedures at various stages of certification, taking into account the influence of the human factor and the protection of confidential information.

A multidisciplinary approach to adequate antibiotic use in a clinical oncology ward.

430 Background: Infectious complications are related to high morbidity and mortality among oncology patients. Despite prompt attention being given to its management, many times the prescription of antibiotics is inadequate or overuse, mainly in palliative care. A multidisciplinary team is essential to take care this complex patient and to support the best practice to antibiotic prescription. Methods: We established a multidisciplinary team with support of medical specialists in clinical oncology (physicians, nurses, pharmacies and residents) and infection control (physician and nurse) to evaluate the antibiotic use, indication and period of use based on local antibiotic stewardship guidelines. Antimicrobial consumption was assessed using Defined daily dose (DDD). A drive diagram was created, and the actions were implemented. The data were collected pre intervention (from January to October 2022) and post intervention (from November 2022 to May 2023). Results: The main therapeutic uses were identified as ceftriaxone (DDD = 134.26), piperacillin-tazobactam (DDD = 112.13), metronidazole (DDD = 134.26) meropenem (DDD= 27.14), and teicoplamine (DDD= 23.69). The final stages of life were the main offender based on our guideline. The average antimicrobial consumption during the final 72 hours of life was 41%. According to these data the following actions were taken: 1) improve the palliative risk identification system; 2) assess the pertinence of antimicrobial use in end-of-life care; 3) creation of the one protocol in this subgroup of patients 4) an applicative was created, allowing the insertion of the advice by the medical team and the follow-up by the pharmaceutical team. In post intervention period, with these multi modal strategies the average consumption in end-of-life care reduced to 18%. Conclusions: A creation of a multidisciplinary team in oncologic care with a particular focus in antibiotic stewardship improved the adherence antibiotics protocols and had an impact in patients end of life.

Six Years of FDA's Postmarket Active Risk Identification and Analysis System (ARIA) in the Sentinel Initiative: Implications for Real World Evidence Generation.

Congress mandated the creation of a postmarket active risk identification and analysis (ARIA) system containing data on 100 million individuals for monitoring risks associated with drug and biologic products using data from disparate sources to complement the US Food and Drug Administration's (FDA's) existing postmarket capabilities. We report on the first six years of ARIA utilization in the Sentinel System (2016 -2021). The FDA has used the ARIA system to evaluate 133 safety concerns; 54 of these evaluations have closed with regulatory determinations while the rest remain in progress. If the ARIA system and FDA's Adverse Event Reporting System are deemed insufficient to address a safety concern, then FDA may issue a postmarket requirement to a product's manufacturer. One-hundred-ninety-seven ARIA insufficiency determinations have been made. The most common situation for which ARIA was found to be insufficient is the evaluation of adverse pregnancy and fetal outcomes following in utero drug exposure, followed by neoplasms and death. ARIA was most likely to be sufficient for thromboembolic events, which have high positive predictive value in claims data alone and do not require supplemental clinical data. The lessons learned from this experience illustrate the continued challenges using administrative claims data, especially to define novel clinical outcomes. This analysis can help to identify where more granular clinical data are needed to fill gaps to improve the use of real-world data for drug safety analyses and provide insights into what is needed to efficiently generate high-quality real-world evidence for efficacy.

Research on the identification and prevention of supply chain finance risks based on DEMATEL-ISM model

This paper subdivides supply chain finance risks according to the market players involved in supply chain finance, uses DEMATEL to classify the status of different risk factors and quantify the degree of influence, and then applies ISM to construct a supply chain finance risk identification system. The study found that the lack of application of new technologies and the poor cooperation among the various actors in the supply chain are the most important factors in supply chain finance risks, and the information flow risk is the main factor that causes many supply chain finance risks. The study found that the lack of application of new technologies and the lack of cooperation among supply chain actors are the most important risks in supply chain finance, and information flow risks are the main factors that cause many supply chain financial risks.

Computational intelligence applied to cybersecurity

Computational intelligence applied to cybersecurity

Study on Control of Risks Existing in the Construction of Information Management System in Public Hospitals

In terms of the medical information management system, it can effectively increase the resource utilization rate of medical and health institutions, optimize the management process, and provide relevant, efficient management approaches, all of which help medical and health institutions meet their internal and external demands to the greatest possible extent. However, the projects related to the information management system in hospitals or other medical institutions are characterized by high system complexity, multiple related links, and uncertainty of project demands due to their own professional and industrial characteristics, which are frequently manifested as many limiting factors in the construction process. It is essential to determine the kind of risk and evaluation methods to explore the risk evaluation indicators and formulate corresponding countermeasures for ongoing medical institution information systems projects. In such a background, this study establishes a perfect risk identification and evaluation system by taking the risk management of projects related to medical institution information system in R hospital as the research object and proposes the corresponding countermeasures as the reference for the establishment of the information management system.

Research on Bank Credit Model Based on Decision Tree

With the rapid development of small and medium-sized enterprises, banks need to establish a sound credit risk identification system. This paper makes a quantitative analysis on the credit risk of 302 enterprises without credit records, and gives the bank's credit strategy for these enterprises when the total annual credit amount is 100 million yuan. Using ID3 decision tree learning algorithm to predict the credit risk level of 302 enterprises, the credit strategies of 302 enterprises are obtained.

An integrated safety implementation analysis based on risk assessment to enhance the safety engineering practice in the gas fields of Sylhet region, Bangladesh

The industrial sectors of Bangladesh mostly depend on the petroleum industry. Historically, risk and hazards are the common scenario in the petroleum industry occurring due to lack of efficient risk assessment, resulting in financial loss, injury, or harm for people, and the environment. To prevent these losses, it is essential to implement the best risk assessment plan.The objective of this research is toenhance the implementation of safety engineering systems in the petroleum industry through risk assessment.
 For this, purpose at first, the questionnaire survey for the perception of risk identification and safety system is performed to determine the current defects in the safety engineering system. Then, using SPSS software the risk analysis as well as ANOVA and reliability analysis of the collected data is accomplished to examine top risk factors. Concerning both interview and questionnaire analysis, an integrated risk assessment-based safety analysishas beenperformed to optimize the performance of the safety engineering system.The outcomes of this research expose the technical, procedural, and behavioral gap in the current risk assessment for the safe implementation of the safety engineering system of the petroleum industry of Bangladesh.

Fuzzy expert system for road type identification and risk assessment of conventional two‐lane roads

AbstractThis paper first presents a fuzzy expert system to identify and classify conventional two‐lane roads based on geometric characteristics. Both fuzzy and neuro‐fuzzy techniques have been used. Fuzzy logic has proved suitable to address this problem, since in this case, there is a variability of input information, and classical rules are not suitable to be used due to the uncertainty introduced by some combinations of the variables. Each road's geometric features were measured by sensors in an equipped vehicle, and are subsequently used to classify the roads according to their real condition. The conventional two‐lane roads used for this research are located in the Madrid Region, in Spain. This intelligent system may be used to update the road database regarding the assigned type to each conventional road, according to their present features and state. Also, a risk identification system has been developed to assess whether a vehicle is driving on a two‐lane road with an inappropriate speed, combining variables such as the former identification model, vehicle type, road longitudinal gradient, the angle covered by each horizontal curve, and the existence or not of an additional traffic lane. A fuzzy risk index is proposed for this approach. This fuzzy model may be useful to detect road sections where safety must be enhanced by revising the speed limit, since less safe situations may arise from travelling at unappropriated speeds.

Research on a Computer Aid Risk Control System Using Classification Capabilities of Support Vector Machines

The paper proposes the use of support vector machines to establish a financial early warning system and a method for financial risk monitoring, gives a financial evaluation index system and its quantitative methods, and uses the classification capabilities of support vector machines to establish a financial early warning model. At the same time, the paper explores and studies the combination of enterprise group’s financial risk identification, assessment, response and management information system, and proposes an enterprise group’s financial risk control information system. Through experimental comparison, the model based on the support vector machine has good consistency with the experimental data, which is in line with the expected results.

Deformation Detection Model of High-Rise Building Foundation Pit Support Structure Based on Neural Network and Wireless Communication

The reasonable selection and optimized design of the deep foundation pit support scheme is directly related to the safety, construction period, and cost of the entire project. Here, based on a large number of theoretical results in many related fields, relevant influencing factors are systematically analyzed, and advanced mathematical algorithms such as neural networks are introduced according to the relevant characteristics of building deep foundation pit support construction. First of all, this paper designs and implements deep foundation pit construction safety risk technology based on wireless communication and BIM technology and analyzes and describes the framework and function of the foundation pit construction safety risk identification system. Secondly, we use neural network algorithms to study the deformation prediction of the foundation pit supporting structure, which can describe the expression method of the above safety knowledge. Finally, the differences and benefits of this method and traditional methods are compared through experiments, which show that this technology can pave the way for the construction of deep foundation pit construction safety risk knowledge.

Ontology knowledge base combined with Bayesian networks for integrated corridor risk warning

With the accelerated urbanization process, the emergence of urban underground integrated pipeline corridors is the trend for cities, especially large and medium-sized cities. However, due to the complexity of the internal system of the integrated corridor, there are various risks in the process of its construction and operation and maintenance, and the risk factors are complex and diverse. In this paper, we introduce ontology technology and knowledge base construction into the risk management of integrated pipeline corridor, build an ontology-based knowledge base of integrated pipeline corridor risk, and construct a Bayesian network based on the established risk knowledge base for risk evaluation of identified risk factors. The combination of ontology knowledge base construction and Bayesian network method of integrated pipeline corridor risk makes the risk identification system completer and more effective, and the method can effectively evaluate the disaster risk level of integrated pipeline corridor operation and maintenance, which can meet the practical needs of integrated pipeline corridor operation and maintenance risk management and disaster prevention and mitigation work.

Research on Risk Identification System Based on Random Forest Algorithm-High-Order Moment Model

With the continuous development of the stock market, designing a reasonable risk identification tool will help to solve the irrational problem of investors. This paper first selects the stocks with the most valuable investment value in the future through the random forest algorithm in the nine-factor model and then analyzes them by using the higher-order moment model to find that different investors’ preferences will make the weight of the portfolio change accordingly, which will eventually make the optimal return and risk set of the composition of the portfolio change. The risk identification system designed in this paper can provide an effective risk identification tool for investors and help them make rational judgments.

Electronic phenotyping of health outcomes of interest using a linked claims-electronic health record database: Findings from a machine learning pilot project.

Claims-based algorithms are used in the Food and Drug Administration Sentinel Active Risk Identification and Analysis System to identify occurrences of health outcomes of interest (HOIs) for medical product safety assessment. This project aimed to apply machine learning classification techniques to demonstrate the feasibility of developing a claims-based algorithm to predict an HOI in structured electronic health record (EHR) data. We used the 2015-2019 IBM MarketScan Explorys Claims-EMR Data Set, linking administrative claims and EHR data at the patient level. We focused on a single HOI, rhabdomyolysis, defined by EHR laboratory test results. Using claims-based predictors, we applied machine learning techniques to predict the HOI: logistic regression, LASSO (least absolute shrinkage and selection operator), random forests, support vector machines, artificial neural nets, and an ensemble method (Super Learner). The study cohort included 32 956 patients and 39 499 encounters. Model performance (positive predictive value [PPV], sensitivity, specificity, area under the receiver-operating characteristic curve) varied considerably across techniques. The area under the receiver-operating characteristic curve exceeded 0.80 in most model variations. For the main Food and Drug Administration use case of assessing risk of rhabdomyolysis after drug use, a model with a high PPV is typically preferred. The Super Learner ensemble model without adjustment for class imbalance achieved a PPV of 75.6%, substantially better than a previously used human expert-developed model (PPV = 44.0%). It is feasible to use machine learning methods to predict an EHR-derived HOI with claims-based predictors. Modeling strategies can be adapted for intended uses, including surveillance, identification of cases for chart review, and outcomes research.

Study on Risks and Countermeasures of Shallow Biogas during Construction of Metro Tunnels by Shield Boring Machine

With the intensive development of cities, the utilization of underground space has attracted more and more attention from industry and academia. Underground rail (metro) in cities has become an imperative mode for people in their daily lives. Meanwhile, the safety of rail tunnel construction has constantly been a challenging issue because of the presence of complex strata containing shallow biogas. Accidents in tunnel construction because of shallow biogas which resulted in massive casualties and property loss have been reported in some recent literature. The excavation of formations containing shallow biogas not only poses a threat to the safety of the earlier stage of tunnel construction but also affects the later operation of metro lines. Therefore, the safety problem caused by shallow biogas should be taken into consideration seriously and avoided in the pre-construction stage. A typical underwater metro tunnel, Hangzhou Metro Line 6, is introduced in this study to suggest the proper approach to deal with the biogas problem during the construction process. The generation mechanism of shallow biogas is clarified and the process of identifying biogas risk during strata exploration is also discussed. A risk identification and control system for shield tunneling through biogas strata is proposed to mitigate the potential dangers of shallow biogas during the construction process. This study provides actual construction experience and countermeasures for other similar metro tunnel projects that encounter biogas strata to diminish the potential risks and avoid severe accidents.

Fire frequency analysis and prediction based on back propagation neural network model and Long-Short Term Memory model

It is reported that there have been many fires in many places this year. This article wants to explore the factors affecting the occurrence of fires and make predictions to prevent the occurrence of forest fires. The correlation between forest indexes and fire frequency in Fujian, Guangxi, Heilongjiang, Hunan, Sichuan, Yunnan and Zhejiang provinces was analyzed based on forest indexes and fire frequency prediction in seven provinces from 1998 to 2018. According to the six relevant indexes of seven provinces in 20 years, the Pearson correlation coefficient between them and the number of fires was analyzed to find the correlation between the variables. The Grey system theory Model (GM (1, 1)) was established to predict the six related index values. The BP neural network model was established based on the historical and the predicted values were substituted into the Back Propagation (BP) neural network model to predict the number of fires. Then, a prediction model based on Long-Short Term Memory (LSTM) was constructed through deep learning library Keras, and the predicted value was substituted into the model to predict the number of fires. Zhejiang Province and Hunan Province were taken as examples to compare the accuracy of the two models’ predictive values. It was found that the LSTM model had better fitting effect, and the LSTM model was finally used to predict the number of future fires. The forest ecological risk identification and prediction system based on Graphical User Interface system (GUI) was developed, the phase space reconstruction of one-dimensional data with time series characteristics was carried out, and the high-dimensional data were trained by using fuzzy neural network to realize data prediction and evaluation. This prediction can be applied to the forest prevention and control departments, and the annual fire situation can be predicted based on historical data, so as to prevent and reduce losses in advance.

Research and application of coal and gas outburst early warning system based on gas geological features

The geological structure of coal mines has always been a dangerous object of attention in coal mine outburst prevention work. In order to realize coal mine safety information management and early warning of gas disasters, comprehensive use of gas geological theory, coal mine disaster warning theory, computer information technology and other analysis methods, considering the influence of geological structure, coal seam occurrence parameters, and gas parameters, an early warning indicator system for identifying the risk of coal and gas outbursts reflecting the geological characteristics of gas has been constructed. The coal and gas outburst risk identification and early warning system is constructed using the principle of multi-index step-by-step identification and extreme value determination, and it is applied on-site in the 3303 Measure Lane in the East Shaft Area of Sihe Mine. The research results show that the constructed early warning system can provide accurate early warning for the area (belt) affected by the geological structure by 10m, and can provide accurate early warning of coal and gas outbursts based on the outburst signs of gas geology such as the thickness of soft layers and changes in coal seam thickness. This technology provides effective support for coal mines to effectively prevent gas disasters and ensure coal mine production safety.

Система идентификации рисков на основе отчета об инциденте в организации здравоохранения

The aim: To assess the risk identification system based on the incident report, the existing incident reporting system, and factors affecting the structure of incidents in the healthcare organization. Methods. Within the framework of this study, personnel were questioned on the knowledge and use of the incident reporting system related to the provision of medical treatment and care. To collect primary information, a questionnaire was developed, statistical processing of the research results was carried out by the method of calculating the relative risk (RR) using the Stat Tech program, on the website "Medical statistics". Results. All employees (100%) are aware of the incident reporting system. Personnel were more likely to use the incident reporting system as a way to deal with business and organizational issues. The knowledge of events that must be reported were rated equally by the doctors and the nurse (70%; RR - 1.0; C1 0.83 - 1.19). The nurse was more afraid of criticism than doctors (56% versus 66%; RR - 1.18; CI 95%; C1 0.94 - 1.47). Doctors rated the priority of filling out a report lower than a nurse (52% versus 58%; RR - 0.89; CI 95%; C1 0.69 - 1.15). The value and convenience of filling out the report were assessed by the doctors and the nurse equally (64%; RR - 1.0; CI 95%; C1 0.82 - 1.23). Conclusion. Medical personnel have a positive view of the incident reporting system, however, lack of knowledge of specific reported events, poor safety culture, and lack of feedback are the main factors hindering the effectiveness of the system. Developing a list of specific health care events to be reported will enhance the effectiveness of incident reporting as a risk management tool. Keywords: healthcare system, risk management, patient safety, incident, incident report.