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Financial Risk Early Warning Based on Big data Analysis 1

Financial Risk Early Warning Based on Big Data Analysis

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Wan, Lanlan School of Economics and Management Shanghai University of Political Science and Law China

ISBN: (纸本)9798350316469

This article proposes a financial risk warning method based on big data analysis, aiming to discover and predict potential financial risks in a timely manner by analyzing and mining a large amount of financial data. This method utilizes advanced big data analysis techniques internal and external information of financial institutions, in order to identify key factors and trends that may trigger risks. Through experimental verification, this method can effectively alert financial risks, improve the risk management level and risk prevention ability of financial institutions. Experiments have also shown that the financial risk warning capability based on big data can be improved by 95%. © 2023 IEEE.

关键词： big data data mining algorithms financial crisis Warning level

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Improving Rule mining for Entry, Descent, and Landing Simulations Using Knowledge Graphs

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2022年第9期19卷 598-612页

作者： De Leon, Samalis Santini Selva, Daniel Way, David W. Texas A&M Univ Dept Aerosp Engn College Stn TX 77843 USA NASA Langley Res Ctr Atmospher Flight & Entry Syst Branch D205 Hampton VA 23666 USA

Designing planetary entry, descent, and landing (EDL) systems requires analyzing large datasets containing tens of thousands of parameters. These datasets are generally manually analyzed by subject-matter experts trying to find interesting correlations and couplings between parameters that explain the behaviors observed. A popular approach to automate the extraction of explanation rules is association rule mining, in which rules with high statistical strength are mined from the dataset. However, current rule mining algorithms generate too many rules that are redundant, too complex, too obvious, or do not make sense to the user. In this paper, we propose a new approach to improve the comprehensibility, insightfulness, and usefulness of the association rules generated during the analysis of an EDL dataset by leveraging a user-provided knowledge graph. The knowledge graph captures the user knowledge about EDL and the specific problem at hand. We then use a statistical relational learning framework based on probabilistic soft logic to assess the degree of consistency of the rule with the user's knowledge of the system. The method is validated in a small study with N=6 subject-matter experts. The results of the study also show interesting relationships between comprehensibility, usefulness, and insightfulness of the extracted rules.

关键词： Model Based System Engineering Probability Density Functions Flight Path Angle data mining algorithms Dynamic Pressure Aerodynamics Monte Carlo Simulation Terrain Relative Navigation Inertial Measurement Unit Artificial Intelligence

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Prediction of fresh herbage yield using data mining techniques with limited plant quality parameters

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Celik, Senol Tutar, Halit Gonulal, Erdal Er, Hasan Bingol Univ Fac Agr Dept Anim Sci Biometry & Genet Unit TR-12000 Bingol Turkiye Bingol Univ Fac Agr Dept Field Crops TR-12000 Bingol Turkiye Bahri Dagdas Int Agr Res Inst TR-42000 Konya Turkiye Bingol Univ Fac Agr Dept Biosyst Engn TR-12000 Bingol Turkiye

The purpose of this study was to ascertain the fresh herbage yield, fertilizer dosage, and plant characteristics of the Sorghum-Sudangrass hybrid grown in arid and semi-arid regions, as well as their interrelationships. For this reason, data from the Sorghum-Sudangrass hybrid were used to assess the predictive performance of several data mining techniques, including CHAID, CART, MARS, and Bagging MARS. Plant traits were measured in Konya and Sanliurfa during 2021 and 2022. The descriptive statistical values were calculated as follows: plant height 306.27 cm, stem diameter 9.47 mm, fresh herbage yield 10852.51 kg/da, crude protein ratio 9.66%, acid detergent fiber 33.39%, neutral detergent fiber 51.85%, acid detergent lignin 9.76%, dry matter digestibility 62.88%, dry matter intake 2.34%, and relative feed value 114.68 (average values). The predictive capacities of the fitted models were assessed using model fit statistics such as the coefficient of determination (R-2), adjusted R-2, root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), and Akaike Information Criterion (AIC). With the lowest values for RMSE, MAPE, SD ratio, and AIC (246, 1.926, 0.085, and 845, respectively), and the highest R-2 value (0.993) and adjusted R-2 value (0.989), the MARS algorithm was determined to be the best model for characterizing fresh herbage yield. As a solid alternative to other data mining techniques, the MARS algorithm was shown to be the most appropriate model for forecasting fresh herbage production.

关键词： data mining algorithms Fertilizer dose Fresh herbage yield Sorghum-sudangrass hybrid

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Using data mining techniques to generate test cases from graph transformation systems specifications

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AUTOMATED SOFTWARE ENGINEERING 2024年第1期31卷 17-17页

作者： Araghi, Maryam Asgari Rafe, Vahid Khendek, Ferhat Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada Goldsmiths Univ London Dept Comp London England Arak Univ Fac Engn Dept Comp Engn Arak *** Iran

Software testing plays a crucial role in enhancing software quality. A significant portion of the time and cost in software development is dedicated to testing. Automation, particularly in generating test cases, can greatly reduce the cost. Model-based testing aims at generating automatically test cases from models. Several model based approaches use model checking tools to automate test case generation. However, this technique faces challenges such as state space explosion and duplication of test cases. This paper introduces a novel solution based on data mining algorithms for systems specified using graph transformation systems. To overcome the aforementioned challenges, the proposed method wisely explores only a portion of the state space based on test objectives. The proposed method is implemented using the GROOVE tool set for model-checking graph transformation systems specifications. Empirical results on widely used case studies in service-oriented architecture as well as a comparison with related state-of-the-art techniques demonstrate the efficiency and superiority of the proposed approach in terms of coverage and test suite size.

关键词： Software testing Model-based testing Test case generation Model checking data mining algorithms Graph transformation systems

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Research on the Impact and Mechanism of Artificial Intelligence on Consumption Upgrading

Research on the Impact and Mechanism of Artificial Intellige...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Zhu, Jixu Chen, Xiaoshi Guangzhou City University of Technology Guangdong Province Guangzhou510800 China

ISBN: (纸本)9798350382693

As the only way to comprehensively expand domestic demand, consumption upgrading gradually reflects the impact and mechanism of consumption upgrading with the development of artificial intelligence technology. The role of mechanism evaluation in consumption structure under artificial intelligence is very important, but there is a problem of inaccurate outcome evaluation. The traditional consumption model cannot solve the problem of evaluation of the mechanism of consumption structure under artificial intelligence, and the evaluation is unreasonable. Therefore, this paper proposes a data mining algorithm to evaluate and analyze the mechanism of optimization and innovation. Firstly, the relative income consumption theory is used to evaluate the consumer market, and the indicators are divided according to the requirements of the mechanism evaluation to reduce the interference factors in the evaluation of the mechanism. Then, the relative income consumption theory evaluates the mechanism of consumption upgrading level, forms a mechanism evaluation scheme, and comprehensively analyzes the evaluation results of the mechanism. MATLAB simulation shows that under certain evaluation criteria, the evaluation accuracy of the mechanism of data mining algorithms on the level of consumption upgrading and the quality of consumption structure transformation and upgrading are better than those of traditional consumption patterns. © 2024 IEEE.

关键词： Artificial intelligence consumption structure data mining algorithms relative income consumption theory

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Dynamic Forecasting of Solar Energy Microgrid Systems Using Feature Engineering

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第6期58卷 7857-7869页

作者： Mohamed, Muamar Mahmood, Farhad E. E. Abd, Mehmmood A. A. Chandra, Ambrish Singh, Bhim Univ Quebec Ecole Technol Super Dept Elect Engn Montreal PQ H3C 1K3 Canada Univ Mosul Dept Elect Engn Mosul 00964 Iraq IBM Corp Austin TX 78758 USA Indian Inst Technol Delhi New Delhi 110016 India

The intermittent and stochastic nature of solar energy generation systems, climate change, and the inefficiency of modern power systems due to zero inertia have created many challenges for on-grid operators. Solar forecasting systems based on machine learning algorithms are an emerging and effective solution that uses Big data related to weather phenomena. However, the predictive ability of these algorithms is hampered by the sporadic nature of solar energy generation. In this article, a robust hybrid machine learning system that utilizes multiple linear regression (MLR) and a Pearson correlation coefficient (PCC) was tested on solar power plant sites of varying capacities in Germany (100-8500 kW). The volume of Big data features can be reduced by focusing on the features that significantly improve the reliability of the mid-term forecasting system. In this way, drastic fluctuations in the prediction of photovoltaic (PV) power generation can be avoided. The results of our approach are evaluated regarding real-world data using the extreme gradient boosting (XGBoost) with feature engineering, and principal component analysis (PCA), in order to forecast PV energy, rank, and track the importance of feature engineering for different PV capacities. Furthermore, we found that the need for selectivity and reduction of performance error was supported by ridge regression. In addition, the proposed novel XGBoost forecast system decreased the root-mean-square error (RMSE) and mean absolute error (MAE) by 30% and 18%, respectively, compared to the Autoencoder and long short-term memory (LSTM) for same datasets. Furthermore, the CoD determination coefficient (R-2) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA).

关键词： Artificial intelligence data mining algorithms forecasting techniques machine learning power system statistical analysis solar microgrid XGBoost algorithm

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Forestry Economic Forecasting Methods, Models and Computer Support Systems 3

Forestry Economic Forecasting Methods, Models and Computer S...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Liu, Yanzhen Liulin County Forestry Bureau Shanxi Liulin030599 China

ISBN: (纸本)9798350319125

With the rapid development of computer technology, computer technology is widely used in all walks of life. The role of computer-supported systems in forestry economic development decision-making is important, but there may be lagging problems in practical applications. Traditional forecasting techniques cannot solve some problems in computer support systems in forestry economic development decision-making, and the evaluation is unreasonable. Therefore, this paper proposes a data mining algorithm for innovative optimization of computer-supported system analysis. Firstly, forestry economic theory is used to evaluate forestry managers, and indicators are divided according to the requirements of the computer support system to reduce them the computer supports the interference factor in the system. Then, the forestry economic theory analyzes the computer support system of forestry economic forecasting, forms a computer support system scheme, and supports the computer system Comprehensive analysis of the program. MATLAB simulation shows that under the condition of certain evaluation criteria, the accuracy of the computer support system of data mining algorithm for forestry economic forecasting is accurate the rationality is better than traditional forecasting techniques. © 2023 IEEE.

关键词： Computer technology data mining algorithms economic forecasts forestry economic theory

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Research on the application of data mining algorithm in photojournalism and short video communication

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第5期11卷

作者： Wang, Yidi Zhang, Yan Song Dalian Polytech Univ Sch Fash Dalian 116034 Liaoning Peoples R China

Short films and photojournalism are crucial parts of video communication, yet there are issues including poor video integration, inconsistency between short videos and photojournalism, and slow communication speed. In order to evaluate and analyse video communication, this research offers a data mining algorithm. First, the database's images and videos are chosen and analysed using a mining algorithm, and then the indicators are reduced by dividing them into groups based on the specifications for video dissemination. video transmission distractions. The mining algorithm then analyses the video propagation, creates a plan that satisfies the criteria, and refines the plans that satisfies the criteria. Analyse. The integration level, timeliness, and compliance rate of data mining algorithms for video propagation are superior to traditional video dissemination method under specific analysis criteria, according to MATLAB simulation.

关键词： photojournalism data mining algorithms short videos Video dissemination

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Addressing data Sparsity in Collaborative Filtering Based Recommender Systems Using Clustering and Artificial Neural Network 11

Addressing Data Sparsity in Collaborative Filtering Based Re...

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IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Althbiti, Ashrf Alshamrani, Rayan Alghamdi, Tami Lee, Stephen Ma, Xiaogang Univ Idaho Dept Comp Sci Moscow ID 83844 USA Taif Univ Dept Informat Technol At Taif 26571 Makkah Saudi Arabia Albaha Univ Dept Comp Sci Albaha 65527 Saudi Arabia Univ Idaho Dept Math & Stat Sci Moscow ID 83844 USA

ISBN: (纸本)9781665414906

Collaborative Filtering (CF) is fundamentally characterized by Recommender Systems (RSs), which have recently attracted researchers' attention. The ever-increasing data about users and items and the emergence of machine learning approaches have motivated the recent development of CF. The sparsity caused by the lack of recorded transactions and data makes it challenging for CF to distinguish between users' similar preferences. As a result of the data sparsity issue, CF ultimately lacks the ability to generate useful recommendations and suffers from poor performance. This paper proposes a novel model that uses clustering and artificial neural network to address the issue of data sparsity in CF. The proposed model CANNBCF, a short name for Clustering and Artificial Neural Network Based Collaborative Filtering, is evaluated using four different datasets from four popular domains (books, music, jokes, and movies). The proposed model shows its superiority to solve the sparsity issue that the traditional CF technique encounters. In this paper, eight experiments are conducted to evaluate the performance of CANNBCF. The evaluation criteria include accuracy, precision, recall, F1-score, and Receiver Operating Characteristics used to examine the proposed model. The results of the experiments show that CANNBCF effectively solves the sparsity issue, improves the quality of recommendations, and demonstrates promising prediction accuracy.

关键词： Collaborative Filtering data mining algorithms data Sparsity Evaluation Recommender Systems

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Dynamic Forecasting of Solar Energy Microgrid Systems Using Feature Engineering

Dynamic Forecasting of Solar Energy Microgrid Systems Using ...

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IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

关键词： Artificial intelligence data mining algorithms forecasting techniques machine learning power system statistical analysis solar microgrid XGBoost algorithm

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