Journal articles on the topic 'Financial engineering Data processing'
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1
Yang, Ning. "Financial Big Data Management and Control and Artificial Intelligence Analysis Method Based on Data Mining Technology." Wireless Communications and Mobile Computing 2022 (May 29, 2022): 1–13. http://dx.doi.org/10.1155/2022/7596094.
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Driven by capital and Internet information (IT) technology, the operating scale and capital scale of modern industrial and commercial enterprises and various organizations have increased exponentially. At present, the manual-based financial work model has been unable to adapt to the changing speed of the modern business environment and the business rhythm of enterprises. All kinds of enterprises and organizations, especially large enterprises, urgently need to improve the operational efficiency of financial systems. By enhancing the integrity, timeliness, and synergy of financial information, it improves the comprehensiveness and ability of analyzing complex problems in financial analysis. It can cope with such rapid changes and help improve the financial management capabilities of enterprises. It provides more valuable decision-making guidance for business operations and reduces business risks. In recent years, the vigorous development of artificial intelligence technology has provided a feasible solution to meet the urgent needs of enterprises. Combining data mining, deep learning, image recognition, natural language processing, knowledge graph, human-computer interaction, intelligent decision-making, and other artificial intelligence technologies with IT technology to transform financial processes, it can significantly reduce the processing time of repetitive basic financial processes, reduce the dependence on manual accounting processing, and improve the work efficiency of the financial department. Through the autonomous analysis and decision-making of artificial intelligence, the intelligentization of financial management is realized, and more accurate and effective financial decision-making support is provided for enterprises. This paper studies the company’s intelligent financial reengineering process, so as to provide reference and reference for other enterprises to upgrade similar financial systems. The results of the analysis showed that at the level of α = 0.05 , there was a significant difference in the mean between the two populations. When the r value is in the range of -1 and 1, the linear relationship between the x and y variables is more obvious. This paper proposes decision-making suggestions and risk control early warning to the group decision-making body, or evaluates the financial impact of the group’s decision-making, and opens the road to financial intelligence.
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Rodríguez-García, Miguel Ángel, Alejandro Rodríguez-González, Ricardo Colomo-Palacios, Rafael Valencia-García, Juan Miguel Gómez-Berbís, and Francisco García-Sánchez. "Using Data Crawlers and Semantic Web to Build Financial XBRL Data Generators: The SONAR Extension Approach." Scientific World Journal 2014 (2014): 1–18. http://dx.doi.org/10.1155/2014/506740.
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Precise, reliable and real-time financial information is critical for added-value financial services after the economic turmoil from which markets are still struggling to recover. Since the Web has become the most significant data source, intelligent crawlers based on Semantic Technologies have become trailblazers in the search of knowledge combining natural language processing and ontology engineering techniques. In this paper, we present the SONAR extension approach, which will leverage the potential of knowledge representation by extracting, managing, and turning scarce and disperse financial information into well-classified, structured, and widely used XBRL format-oriented knowledge, strongly supported by a proof-of-concept implementation and a thorough evaluation of the benefits of the approach.
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Pollak, Ilya. "Statistics and Data Analysis for Financial Engineering (Ruppert, D.; 2011) [Book Reviews]." IEEE Signal Processing Magazine 28, no. 5 (September 2011): 146–47. http://dx.doi.org/10.1109/msp.2011.941994.
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Cont, Rama. "Statistical Modeling of High-Frequency Financial Data." IEEE Signal Processing Magazine 28, no. 5 (September 2011): 16–25. http://dx.doi.org/10.1109/msp.2011.941548.
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G, Rohith Urs, Nithin D, Akul G. Devali, and Rakshit Vastrad. "Analysis of Text Data For Stock Prediction." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3391–95. http://dx.doi.org/10.22214/ijraset.2022.43132.
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Abstract: Accounting for price fluctuations and understanding people's emotions can help to improve stock price forecasting. Only a few models can decipher financial jargon and have stock price change datasets that have been labelled. In this project, we used text mining techniques to extract high-quality data from news and tweets published by legitimate businesses on the internet, allowing us to analyse, decide, and update our database for future use. In this paper, we propose an information gathering and processing framework that combines a natural language processing tool with our algorithms. We use natural language processing and machine learning techniques to make predictions. The result demonstrates the algorithm's ability to foresee favorable outcomes.
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Wang, Qianyi. "Research on University Financial Accounting Management System Based on Big Data and Blockchain Data Fusion." Wireless Communications and Mobile Computing 2022 (September 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/4118075.
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The majority of traditional finance management in colleges and universities is manual. This backward management mode brings a lot of inconvenience to financial processing. An essential concern in the daily financial administration of colleges and universities is how to efficiently gather, handle, and evaluate this important financial information and apply this beneficial knowledge to the daily management of colleges and universities. College and university finance administration has become increasingly complex due to the rapid growth of these institutions. The standard financial accounting management system is far from adequate for the daily needs of colleges and universities as their size grows. As a result, a new financial accounting management system for universities is being developed based on the combination of big data and blockchain data. The embedded processor, DDR2 memory chip, network interface, and USB interface are all designed by the hardware section. The software element examines the needs of university financial accounting management before designing a model based on big data and blockchain integration for university financial accounting management. Finally, the financial accounting management function module and database are designed and tested. The experimental results show that the designed financial accounting management system can effectively carry out financial management, has good performance, and has certain application value.
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Drakakis, Konstantinos. "Application of signal processing to the analysis of financial data [In the Spotlight]." IEEE Signal Processing Magazine 26, no. 5 (September 2009): 160–58. http://dx.doi.org/10.1109/msp.2009.933377.
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Zhu, Jian Peng, Pei Pei Wang, Ding Wang, and Ying Wang. "The Research of Technical Architecture of Semi-Structured XBRL Data Based on Hadoop Cluster and Structured Data Exchange System." Applied Mechanics and Materials 678 (October 2014): 130–34. http://dx.doi.org/10.4028/www.scientific.net/amm.678.130.
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With the fast propulsion of the informatization, a large base of semi-structured XBRL data and structured financial data have been accumulated in various business activities, including production, commercial management, transaction, government supervising and administrating, which is ever-increasing with a relatively large-scale. These huge amounts of business data surely will be distributed on the cloud in the future. Therefore, the purpose of this article is to solve this common challenging problem of how to share the huge number of structured financial data and semi-structured XBRL data. Combining the related cloud computing technology with the XBRL technology, a technical architecture and implementation of semi-structured XBRL data and structured data exchange system based on Hadoop cluster is proposed in this study. The technical architecture has certain engineering value and theoretic significance in the area of big data processing and sharing.
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Al-qerem, Ahmad, Ghazi Al-Naymat, Mays Alhasan, and Mutaz Al-Debei. "Default Prediction Model: The Significant Role of Data Engineering in the Quality of Outcomes." International Arab Journal of Information Technology 17, no. 4A (July 31, 2020): 635–44. http://dx.doi.org/10.34028/iajit/17/4a/8.
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For financial institutions and the banking industry, it is very crucial to have predictive models for their core financial activities, and especially those activities which play major roles in risk management. Predicting loan default is one of the critical issues that banks and financial institutions focus on, as huge revenue loss could be prevented by predicting customer’s ability not only to pay back, but also to be able to do that on time. Customer loan default prediction is a task of proactively identifying customers who are most probably to stop paying back their loans. This is usually done by dynamically analyzing customers’ relevant information and behaviors. This is significant so as the bank or the financial institution can estimate the borrowers’ risk. Many different machine learning classification models and algorithms have been used to predict customers’ ability to pay back loans. In this paper, three different classification methods (Naïve Bayes, Decision Tree, and Random Forest) are used for prediction, comprehensive different pre-processing techniques are being applied on the dataset in order to gain better data through fixing some of the main data issues like missing values and imbalanced data, and three different feature extractions algorithms are used to enhance the accuracy and the performance. Results of the competing models were varied after applying data preprocessing techniques and features selections. The results were compared using F1 accuracy measure. The best model achieved an improvement of about 40%, whilst the least performing model achieved an improvement of 3% only. This implies the significance and importance of data engineering (e.g., data preprocessing techniques and features selections) course of action in machine learning exercises
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Zhang, Zhongmin. "Intelligent Optimization of the Financial Sharing Path Based on Accounting Big Data." Mathematical Problems in Engineering 2022 (October 11, 2022): 1–8. http://dx.doi.org/10.1155/2022/1310994.
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In order to solve the problems of inconsistent accounting and untimely accounting information, this paper proposes a research method of intelligent optimization of financial sharing path based on big data of accounting. This paper analyzes the current working situation of the financial sharing service center of large- and medium-sized enterprises and expounds the reasons for the blockchain. The author embeds the blockchain technology in the financial sharing service center, with a view to optimizing the design of the current FSSC’s functions in terms of the scope of daily business work, the effectiveness of accounting information processing, and the utilization of financial analysis decisions, and finally analyzes the corporate effect of applying the financial sharing architecture based on blockchain technology. The results show that compared with the financial sharing score under the traditional mode, the financial sharing score under the blockchain technology is higher, and the former is 69.675 points, and the latter is 80.6340 points. From this, we can conclude that financial sharing under blockchain technology has significant advantages. This framework realizes the effective allocation of enterprise information resources through finance to achieve real industry finance integration.
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Li, Bohua, and Ge Li. "Function Extraction Based on CFPS and Digital Financial Index: Data Mining Techniques for Prognosis of Operational Risks of Financial Institutions." Journal of Sensors 2022 (August 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/9645142.
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Financial deregulation, financial globalization, and the increasing variety and technological sophistication of the commodities offered by financial services have made the operations of financial institutions more complex. Compared with credit risk and market risk, financial institutions’ transaction risk management plays an increasingly important role in financial practice. As an emerging technology, big data mining technology has a unique advantage in optimizing the processing and management of large amounts of data. Big data mining technology not only has the common functions of finding, comprehensively managing all kinds of information, collecting and analyzing data, and conducting statistics but also should have the ability to process information that is hidden and useful in the database through data mining technology. Based on CFPS and data mining technology, this paper analyzes the operational risk of financial institutions, analyzes the causes of the operational risk of financial institutions, discusses the measures to avoid the operational risk of financial institutions, and draws corresponding conclusions.
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Zhang, Xiao-ping, and David Kedmey. "TechWare: Financial Data and Analytic Resources [Best of the Web]." IEEE Signal Processing Magazine 28, no. 5 (September 2011): 138–41. http://dx.doi.org/10.1109/msp.2011.941840.
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Zou, Jie, Wenkai Gong, Guilin Huang, Gebiao Hu, and Wenbin Gong. "Research on the Improvement of Big Data Feature Investment Analysis Algorithm for Abnormal Trading in the Financial Securities Market." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 406–12. http://dx.doi.org/10.46300/9106.2022.16.50.
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Traditional investment analysis algorithms usually only analyze the similarity between financial time series and financial data, which leads to inaccurate and inefficient analysis of investment characteristics. In addition, the trading volume of financial securities market is huge, the amount of investment data is also very large, and the detection of abnormal transactions is difficult. The aim of feature extraction is to obtain mathematical features that can be recognized by machine. Different from the traditional methods, this paper studies and improves the big data investment analysis algorithm of abnormal transactions in financial securities market. After processing the captured trading data of financial securities market, the big data feature of abnormal trading is extracted. Combined with the abnormal trading and the financial securities market, the investment strategy is determined. The optimization objective function is set and the genetic algorithm is used to improve the investment analysis algorithm. The simulation experiment verifies the improved investment analysis algorithm, and the average Accuracy of investment analysis is increased by at least 11.24%, the ROI is significantly improved, and the efficiency is higher, which indicates that the proposed algorithm has ideal application performance.
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Alotia, Martha Ruth, Alprita Parasala, Eliyah Acantha M. Sampetoding, Inriawati Parauba, and Yus Martin Sipota. "Perancangan Sistem Informasi Pengolahan Administrasi Keuangan Pada Dinas P3A-PMD Kabupaten Kepulauan Talaud." JTIM : Jurnal Teknologi Informasi dan Multimedia 3, no. 3 (November 15, 2021): 152–60. http://dx.doi.org/10.35746/jtim.v3i3.163.
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Financial Administration Processing is a process that converts financial data into information, in carrying out this process often using a computer so that it can run automatically. Through a data processing system, it can facilitate organizations to manage financial data for related fields so that they can help complete activities in the financial sector, especially for the process of recording and financial reports quickly. The purpose of this study is to find out how efficient and accurate the information system is at the Office of Women's Empowerment, Child Protection, Community and Village Empowerment in the Talaud Islands Regency (P3A-PMD). The method used in this study is a qualitative method by finding out how efficient and accurate the information system is currently running at the P3A-PMD Office. Data was collected by means of observation, interviews, and literature study. Sources of data used are primary data and secondary data. Data management with a computer is expected to help the process of recording data so that data storage will be better and if an error occurs, you can directly edit the data.
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Lopata, Audrius, Saulius Gudas, Rimantas Butleris, Vytautas Rudžionis, Liutauras Žioba, Ilona Veitaitė, Darius Dilijonas, Evaldas Grišius, and Maarten Zwitserloot. "Financial Data Anomaly Discovery Using Behavioral Change Indicators." Electronics 11, no. 10 (May 17, 2022): 1598. http://dx.doi.org/10.3390/electronics11101598.
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In this article we present an approach to financial data analysis and anomaly discovery. In our view, the assessment of performance management requires the monitoring of financial performance indicators (KPIs) and the characteristics of changes in KPIs over time. Based on this assumption, behavioral change indicators (BCIs) are introduced to detect and evaluate the changes in traditional KPIs in time series. Three types of BCIs are defined: absolute change indicators (BCI-A), relative change indicators (ratio indicators BCI-RE), and delta change indicators (D-BCI). The technique and advantages of using BCIs to identify unexpected deviations and assess the nature of KPI value changes in time series are discussed and illustrated in case studies. The architecture of the financial data analysis system for financial data anomaly detection is presented. The system prototype uses the Camunda business rules engine to specify KPIs and BCI thresholds. The prototype was successfully put into practice for an analysis of actual financial records (historical data).
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Gołębiewska, Barbara, Oksana Voronko, and Monika Gębska. "COMPARISON OF THE FINANCIAL CONDITION OF MILK PROCESSING ENTERPRISES IN POLAND AND UKRAINE." Annals of the Polish Association of Agricultural and Agribusiness Economists XXIV, no. 4 (December 10, 2022): 53–64. http://dx.doi.org/10.5604/01.3001.0016.1382.
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The study aimed to compare and evaluate the effectiveness of enterprises processing and trading dairy products in Poland and Ukraine. Milk processing is an important sector of food production. Milk and milk products account for about 14% of world trade in agricultural products. The five largest companies from Poland and Ukraine were selected for the study. Evaluation of the effectiveness of dairy enterprises required the adoption of appropriate indices. Therefore, the research considered, above all, the indices characterizing the profitability of the surveyed companies as the basic ones in the evaluation of the financial condition of enterprises. In addition, the evaluation included the surveyed enterprises’ return on sales, assets, and equity. The analyses were performed based on data available in the EMIS database for 2016-2020. In Poland, the milk processing sector was in a relatively good financial condition, although the profitability ratios were not too high. Profitability ratios have long been lower than the food industry average and have fallen even further in recent years. The data show that the return on equity slightly exceeded the interest rate on bank deposits and treasury bonds. In Ukrainian enterprises, the return on equity, assets, and sales was much higher, but it was also characterized by high volatility.
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Domagała, Joanna. "INTENSITY OF INTERNATIONALIZATION OF FOOD INDUSTRY SECTORS AND THEIR ECONOMIC AND FINANCIAL RESULTS." Annals of the Polish Association of Agricultural and Agribusiness Economists XXIII, no. 2 (June 29, 2021): 41–50. http://dx.doi.org/10.5604/01.3001.0015.0027.
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The aim of the article is to determine the relationship between the level of internationalization of food industry sectors and their economic and financial results. Unpublished CSO data for the years 2000-2017 were used. The data concerned the processing of fish, milk, meat, fruit and vegetables, beverage production, bakery and flour products, oils and fats, grain mill products, starches and starch products, ready-made feed and animal feed. To conduct the analysis, the internationalization intensity index calculated for individual food sectors was used. The analyzed sectors of the food industry were divided into 3 groups according to the intensity of internationalization. The most internationalized sectors were fish processing and the production of tobacco products, and the least internationalized sectors were milk and meat processing. In the next step, selected groups were compared in terms of economic and financial indicators. In order to confirm the statistical significance of the diagnosed differences, the Kruskal-Wallis rank sum test was used. The research confirmed that the increase in the internationalization of the food industry sectors mainly affects indicators related to the labor factor, technical progress and asset productivity.
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Xu, Qingzhen. "A Novel Machine Learning Strategy Based on Two-Dimensional Numerical Models in Financial Engineering." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/659809.
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Machine learning is the most commonly used technique to address larger and more complex tasks by analyzing the most relevant information already present in databases. In order to better predict the future trend of the index, this paper proposes a two-dimensional numerical model for machine learning to simulate major U.S. stock market index and uses a nonlinear implicit finite-difference method to find numerical solutions of the two-dimensional simulation model. The proposed machine learning method uses partial differential equations to predict the stock market and can be extensively used to accelerate large-scale data processing on the history database. The experimental results show that the proposed algorithm reduces the prediction error and improves forecasting precision.
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Wang, Yao. "Financial Early Warning Model for Listed Companies Based on the Smart Sensor Data Network." Journal of Sensors 2022 (September 5, 2022): 1–13. http://dx.doi.org/10.1155/2022/7666354.
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This paper uses intelligent sensors to build a data network, collects information about listed enterprises in all aspects, performs frequency statistics and semantic analysis based on the financial domain lexicon on information related to listed enterprises, and introduces big data variables into a nonlinear support vector machine early warning model of enterprises combined with financial indicators. This paper introduces online reviews as big data indicators based on financial early warning theories and methods. Suitable financial indicators and big data indicators are selected for the financial early warning model to filter the available indicators. The prediction results of only financial indicators are compared with the prediction results of incorporating big data indicators. Quantify the comment information related to enterprises on the platform through sentiment classification and statistics on the number of comment information posted. The cost-sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm to build a dynamic imbalance warning model. To address the problems of large complexity and computation of unbalanced big data classification, high reliance on a priori knowledge, and classification performance to be improved, the classification and detection method of the intelligent sensor data network is proposed. By introducing migration learning, the problem of knowledge acquisition and training efficiency of high-dimensional complex data feature extraction in a big data environment is effectively solved, and the network performance is optimized by a conjugate gradient descent algorithm. Through simulation experiments, the prediction accuracy of the model for positive class samples is significantly improved after the nonequilibrium processing, the recall rate of the network model reaches 84.15%, and the prediction accuracy of the network model under different time steps reaches more than 90%. The experiment proves that the model can send out financial alert information more quickly and efficiently and accurately when a financial crisis may occur, relative to the traditional financial forecasting methods.
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Colling, D., D. Britton, J. Gordon, S. Lloyd, A. Doyle, P. Gronbech, J. Coles, et al. "Processing LHC data in the UK." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1983 (January 28, 2013): 20120094. http://dx.doi.org/10.1098/rsta.2012.0094.
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The Large Hadron Collider (LHC) is one of the greatest scientific endeavours to date. The construction of the collider itself and the experiments that collect data from it represent a huge investment, both financially and in terms of human effort, in our hope to understand the way the Universe works at a deeper level. Yet the volumes of data produced are so large that they cannot be analysed at any single computing centre. Instead, the experiments have all adopted distributed computing models based on the LHC Computing Grid. Without the correct functioning of this grid infrastructure the experiments would not be able to understand the data that they have collected. Within the UK, the Grid infrastructure needed by the experiments is provided by the GridPP project. We report on the operations, performance and contributions made to the experiments by the GridPP project during the years of 2010 and 2011—the first two significant years of the running of the LHC.
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He, Han, Yuanyuan Hong, Weiwei Liu, and Sung-A. Kim. "Data mining model for multimedia financial time series using information entropy." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5339–45. http://dx.doi.org/10.3233/jifs-189019.
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At present, KDD research covers many aspects, and has achieved good results in the discovery of time series rules, association rules, classification rules and clustering rules. KDD has also been widely used in practical work such as OLAP and DW. Also, with the rapid development of network technology, KDD research based on WEB has been paid more and more attention. The main research content of this paper is to analyze and mine the time series data, obtain the inherent regularity, and use it in the application of financial time series transactions. In the financial field, there is a lot of data. Because of the huge amount of data, it is difficult for traditional processing methods to find the knowledge contained in it. New knowledge and new technology are urgently needed to solve this problem. The application of KDD technology in the financial field mainly focuses on customer relationship analysis and management, and the mining of transaction data is rare. The actual work requires a tool to analyze the transaction data and find its inherent regularity, to judge the nature and development trend of the transaction. Therefore, this paper studies the application of KDD in financial time series data mining, explores an appropriate pattern mining method, and designs an experimental system which includes mining trading patterns, analyzing the nature of transactions and predicting the development trend of transactions, to promote the application of KDD in the financial field.
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Wan, Jing Xian, Feng Ying He, Bin Liu, and Shang Ping Zhong. "A Monitoring Model of Unusual Transactions Based on Complex Event Processing." Advanced Materials Research 791-793 (September 2013): 845–51. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.845.
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Faced with the demand for real-time "big data" processing, the existing financial risk early warning systems are generally difficult to identify hidden risks in massive data information quickly and accurately. This paper introduces a complex event processing technology (CEP), proposes a method of real-time monitoring for "big data", establishes a monitoring model of unusual transactions, designs and realizes a system based on this model. The model contains data acquisition and encapsulation module, custom rules modeling module and results display module. Using real data of a security company to test the system, the results show that, it can identify hidden risks in unusual transactions accurately, and the speed of processing is improved significantly comparing with the system which is based on traditional database analysis method.
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Rudžionis, Vytautas, Audrius Lopata, Saulius Gudas, Rimantas Butleris, Ilona Veitaitė, Darius Dilijonas, Evaldas Grišius, Maarten Zwitserloot, and Kristina Rudzioniene. "Identifying Irregular Financial Operations Using Accountant Comments and Natural Language Processing Techniques." Applied Sciences 12, no. 17 (August 26, 2022): 8558. http://dx.doi.org/10.3390/app12178558.
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Finding not typical financial operations is a complicated task. The difficulties arise not only due to the sophisticated actions of fraudsters but also because of the large number of financial operations performed by business companies. This is especially true for large companies. It is highly desirable to have a tool to reduce the number of potentially irregular operations significantly. This paper presents an implementation of NLP-based algorithms to identify irregular financial operations using comments left by accountants. The comments are freely written and usually very short remarks used by accountants for personal information. Implementation of content analysis using cosine similarity showed that identification of the type of operation using the comments of accountants is very likely. Further comment content analysis and financial data analysis showed that it could be expected to reduce the number of potentially suspicious operations significantly: analysis of more than half a million financial records of Dutch companies enabled the identification of 0.3% operations that may be potentially suspicious. This could make human financial auditing easier and more robust task.
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Peng, Kuashuai, and Guofeng Yan. "A survey on deep learning for financial risk prediction." Quantitative Finance and Economics 5, no. 4 (2021): 716–37. http://dx.doi.org/10.3934/qfe.2021032.
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<abstract> <p>The rapid development of financial technology not only provides a lot of convenience to people's production and life, but also brings a lot of risks to financial security. To prevent financial risks, a better way is to build an accurate warning model before the financial risk occurs, not to find a solution after the outbreak of the risk. In the past decade, deep learning has made amazing achievements in the fields, such as image recognition, natural language processing. Therefore, some researchers try to apply deep learning methods to financial risk prediction and most of the results are satisfactory. The main work of this paper is to review the predecessors' work of deep learning for financial risk prediction according to three prominent characteristics of financial data: heterogeneity, multi-source, and imbalance. We first briefly introduced some classical deep learning models as the model basis of financial risk prediction. Then we analyzed the reasons for these characteristics of financial data. Meanwhile, we studied the differences of commonly used deep learning models according to different data characteristics. Finally, we pointed out some open issues with research significance in this field and suggested the future implementations that might be feasible.</p> </abstract>
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Priyono, Nuwun, Risma Wira Bharata, and Ari Nurul Fatimah. "Analisis Penilaian Kondisi Keuangan Pada Pemerintah Kabupaten Magelang Periode Tahun 2015-2019." Jurnal Akuntansi dan Pajak 22, no. 1 (July 27, 2021): 244. http://dx.doi.org/10.29040/jap.v22i1.2347.
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This study aims to analyze and assess trends in the financial condition of the Government of Magelang Regency which includes: financial factors and environmental factors. This study uses secondary data. The data analyzed includes data on financial reports in the Magelang Regency Government such as: balance sheet, APBD, budget realization reports, and operational reports. The analysis year period starts from 2015-2019. Analysis of the financial condition assessment using the Fiscal Tren Monitoring System (FTMS) model in the Magelang Regency Government. The research method used in this research is to use a descriptive approach based on secondary data processing and coupled with numerical analysis. The analysis technique in this research uses descriptive statistics, namely: presenting data in the form of tables, graphs, averages and percentage calculations. Analysis of the financial condition assessment using the FTMS model in Magelang Regency 2015-2019 shows that the financial factor includes 4 indicators consisting of 9 sub indicators. Of the 9 financial sub indicators, 5 sub indicators have the expected trend and 4 sub indicators have an unexpected trend. Furthermore, environmental factors consist of 1 indicator which includes 5 sub indicators. Of the 5 sub indicators, 2 sub indicators have the expected trend and 3 sub indicators have less expected trends for the assessment of the financial condition of the Magelang Regency government.
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Radha, K., and B. Thirumala Rao. "A Study on Big Data Techniques and Applications." International Journal of Advances in Applied Sciences 5, no. 2 (June 1, 2016): 101. http://dx.doi.org/10.11591/ijaas.v5.i2.pp101-108.
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<p>We are living in on-Demand Digital Universe with data spread by users and organizations at a very high rate. This data is categorized as Big Data because of its Variety, Velocity, Veracity and Volume. This data is again classified into unstructured, semi-structured and structured. Large datasets require special processing systems; it is a unique challenge for academicians and researchers. Map Reduce jobs use efficient data processing techniques which are applied in every phases of Map Reduce such as Mapping, Combining, Shuffling, Indexing, Grouping and Reducing. Big Data has essential characteristics as follows Variety, Volume and Velocity, Viscosity, Virality. Big Data is one of the current and future research frontiers. In many areas Big Data is changed such as public administration, scientific research, business, The Financial Services Industry, Automotive Industry, Supply Chain, Logistics, and Industrial Engineering, Retail, Entertainment, etc. Other Big Data applications are exist in atmospheric science, astronomy, medicine, biologic, biogeochemistry, genomics and interdisciplinary and complex researches. This paper is presents the Essential Characteristics of Big Data Applications and State of-the-art tools and techniques to handle data-intensive applications and also building index for web pages available online and see how Map and Reduce functions can be executed by considering input as a set of documents.</p><p> </p>
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Chen, Yanqing. "Enterprise Financial Data Sharing Based on Information Fusion Cloud Computing Environment." Wireless Communications and Mobile Computing 2022 (January 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/5994628.
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At present, many companies have many problems such as high financial costs, low financial management capabilities, and redundant frameworks; at the same time, the SASAC requires that the enterprise’s financial strategy transfer from “profit-driven” to “value-driven”, finance separate from accounting to improve the operational efficiency of the company. Under this background, more and more enterprise respond to the call of the SASAC; in order to achieve the goals of corporate financial cost savings and financial management efficiency improved, we began to provide services through financial sharing. The research of information fusion theory involves many basic theories, which can be roughly divided into two large categories from the algorithmic point of view: probabilistic statistical method and artificial intelligence method. The main task of artificial intelligence is to realize the computer for some learning, thinking process, and wisdom formation of simulation, and an important goal of information integration is the human brain comprehensive processing ability simulation, so artificial intelligence method will have broad application prospects in the field of information fusion; the common methods have D-S evidence reasoning, fuzzy theory, neural network, genetic algorithm, rough set, and other information fusion methods. The purpose of this paper is to proceed from the internal financial situation of the enterprise, analyze data security issues in the operation of financial shared services, and find a breakthrough in solving problems. But, with constantly expanding of enterprise group financial sharing service scale, the urgent problem to be solved is how to ensure the financial sharing services provided by enterprises in the cloud computing environment. This paper combines financial sharing service theory and information security theory and provides reference for building financial sharing information security for similar enterprises. For some enterprise that have not established a financial shared service center yet, they can learn from the establishment of the financial sharing information security system in this paper and provide a reference for enterprise to avoid the same types of risks and problems. For enterprise that has established and has begun to practice a financial shared information security system, appropriate risk aversion measures combined with actual situation of the enterprise with four dimensions related to information security system optimization was formulated and described in this paper. In summary, in the background of cloud computing, financial sharing services have highly simplified operational applications, and data storage capabilities and computational analysis capabilities have been improved greatly. Not only can it improve the quality of accounting information but also provide technical support for the financial sharing service center of the enterprise group, perform financial functions better, and enhance decision support and strategic driving force, with dual practical significance and theoretical significance.
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Lu, Mei, and Qing Bo Hao. "Research on Construction Engineering with Data Processing in the Endowment Real Estate Financing Efficiency Evaluation." Applied Mechanics and Materials 730 (January 2015): 339–42. http://dx.doi.org/10.4028/www.scientific.net/amm.730.339.
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China's ageing population, Pension construction engineering is imperative, the root cause of the slow development in China's pension cause is money problems, project financing is the preferred way for solve this problemProject financing as a new financing mode in the world grew rapidly and is becoming more and more get people's attention.Investment project is feasible and can smooth implementation, to a large extent depends on the success or failure of project financing.Especially old real estate investment is high, long payback period, will not be able to attract developers investment restricts the development of China's aging property, the reasonable choice of the mode of financing is of great significance to endowment real estate development, based on summarizing the domestic and foreign existing various financing model to analyze it, and then in the same quadrant of the financing mode F - AHP analysis method, according to different project financing mode for further optimization.
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Wang, Yijie. "Research on Supply Chain Financial Risk Assessment Based on Blockchain and Fuzzy Neural Networks." Wireless Communications and Mobile Computing 2021 (February 16, 2021): 1–8. http://dx.doi.org/10.1155/2021/5565980.
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With the development of supply chain finance, the credit risk of small- and medium-sized financing enterprises from the perspective of supply chain finance has arisen. Risk management is one of the key tasks of the credit business of banks and other financial institutions, which runs through all aspects of the credit business before, during, and after the loan. This article combines blockchain and fuzzy neural network algorithms to study the credit risk of SME financing from the perspective of supply chain finance. This article builds a supply chain financial system through blockchain technology and integrates supply chain financial information into blocks. The fuzzy neural network algorithm is used for financial data processing and risk assessment, effectively solving and improving the risk processing level of the supply chain. Through further simulation, the application effect of blockchain and machine learning algorithms in the supply chain financial system was verified.
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Jarwono, Reski, Azwir Nasir, and Arumega Zarefar. "Determinasi Tingkat Pengungkapan Laporan Keuangan." Jurnal Akuntansi Keuangan dan Bisnis, Vol.13 No. 2 (2020) (November 30, 2020): 50–59. http://dx.doi.org/10.35143/jakb.v13i2.4359.
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This study aims to determine the effect of company size, leverage, liquidity, earnings management, ceo duality, quality of directors and foreign share ownership on the level of financial statement disclosure. The data used in this study are secondary data. The method used for sample selection was purposive sampling. The number of samples in this study were 96 companies. Hypothesis testing in this study was carried out using the t statistical test. The data analysis technique used in this study is multiple linear regression analysis and moderated regression analysis using the statistical product and service solution (SPSS) version 20.0 for windows data processing software program. The results of this study indicate that company size, liquidity, ceo duality, and quality of directors have a significant effect on the level of financial statement disclosure. Meanwhile, leverage, earnings management and foreign share ownership have no effect on the level of financial statement disclosure
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Gaol, Ford Lumban, Lufty Abdillah, and Tokuro Matsuo. "Adoption of Business Intelligence to Support Cost Accounting Based Financial Systems — Case Study of XYZ Company." Open Engineering 11, no. 1 (November 19, 2020): 14–28. http://dx.doi.org/10.1515/eng-2021-0002.
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AbstractXYZ is a company engaged in the port sector. To support the company’s business processes, XYZ uses two applications to carry out operational activities, namely the CARTOS application to manage invoices and the Finance application to record company costs and revenues. To produce a cost accounting report, XYZ is still processing and visualizing it manually with data sources from the two applications mentioned earlier. This resulted in quite a long time processing data into information. So that reporting to management cannot be done in real time. Therefore XYZ needs a system that can help management to analyze and manage data into information in real time. The Business Intelligence (BI) method is one of the solutions for company needs, especially in analyzing and providing access to data to help make better decisions.This study discusses the design and implementation of business intelligence solutions ranging from architecture, data warehouse, ETL processes and visualization in the form of a dashboard in accordance with the needs of XYZ. The method used in developing business intelligence dashboards refers to the executive information system life cycle method which consists of justification, planning, business analysis, design, construction, and dissemination. The results of this research are dashboard visualization using the Power BI tool that displays information and knowledge needed in the monitoring process and becomes material to produce management decisions related to cost accounting reports.
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Zhou, Jing, Ong Tze San, and Yan Zhu. "A New Transparent and Secured Transmission Routing Method for Blockchain Data in Management Systems." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (November 30, 2022): 141–51. http://dx.doi.org/10.17762/ijritcc.v10i11.5801.
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A significant quantity of information, particularly financial data, has now been growing in accordance with the advancement of information technologies. Due to the prevalence of fraud, it is impossible to identify the data sources for corporate financial data and the applicable employees. There are substantial issues with nonstandard behavior and a lack of critical financial data of these firms, as evidenced by the fact that the majority of employees are not capable of perform appropriate queries on the needed financial statements. It has consistently created financial information management for companies more difficult, posed a risk to the entire company's ecosystem, and hurt the interests of several parties, among other concerns, because many analogous concerns were not satisfactorily addressed. Blockchain has garnered a great deal of attention recently, and Crypto currency and other crypto currencies have gained popularity as a result. This is because of the characteristics of blockchain, like centralized control, confidentiality, truthfulness, and lack of courage, which make data difficult to predict and tamper with. According to current implementation and exploration, blockchain has emerged as a novel solution to issues relating to company financial information management since these features are connected to the data storage privacy and data transfer speed required by this type of management. In order to construct a transparent and secured transmission method for blockchain data, the study deployed blockchain for managing the financial management framework and information processing strategy. In terms of security level, throughput, run time, and scalability, the suggested Blockchain solution is contrasted with existing approaches.
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Zhong, Ran, Qianying Zhang, and Yachao Zhao. "Research on Enterprise Financial Accounting Information Security Model Based on Big Data." Wireless Communications and Mobile Computing 2022 (May 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/7929846.
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Powered by the rapid development of the information age, big data technology has had a great impact on China’s socialist economic development. Big data technology is integrated into all fields of nowadays society, especially in the accounting industry of enterprises. Accounting computerization quickly replaces the traditional manual bookkeeping, realizes the timeliness and accuracy of accounting information data processing, and improves the work efficiency and quality of accounting staff to a great extent, but at the same time, the security of financial accounting information is also very prominent. To tackle this problem, this paper proposes a hybrid encryption algorithm based on double chaotic system and improved AES encryption algorithm. The improved AES algorithm uses affine transformation pairs (A7 and 6F) to generate new S-boxes. The double four-dimensional hyperchaotic system is transformed from two three-dimensional chaotic systems, and then, the transformed hyperchaotic system is used to generate chaotic sequences, and a block encryption scheme is designed. On Hadoop big data platform, double hyperchaotic encryption scheme and improved AES algorithm are combined. Simulation test results show that this method can safely transmit enterprise financial accounting information data packets. With the increase of computing cluster nodes, its encryption transmission efficiency continues to improve. This scheme not only solves the problem of enterprise financial accounting data security encryption but also realizes the parallel transmission of encrypted data in the information age, forming a double guarantee of data transmission security and efficiency.
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Potipiti, Tanapong, and Win Supanwanid. "Data augmentation for stock return prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (December 1, 2022): 1563. http://dx.doi.org/10.11591/ijai.v11.i4.pp1563-1569.
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In the last decade, there have been advances in machine learning performance in various domains, including image classification, natural language processing, and speech recognition. The increase in the size of training data is essential for the improvement in these domains. The two ways to have larger training sets are acquiring more original data and employing effective data augmentation techniques. However, in stock prediction studies, the sizes of datasets have not changed much and there is no accepted data augmentation technique. Consequently, there has been no similar progress in stock prediction. This paper proposes an intuitive and effective data augmentation technique for stock return prediction. New synthetic stocks are generated from linear combinations of original stocks. Unlike previous studies, our augmentation mimics actual financial asset creation processes. Our data augmentation significantly improves prediction accuracy. Moreover, we investigate how the characteristics of original data affect the data augmentation performance. We find a U-shape relationship between accuracy improved from the augmentation and return correlation in original data.
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Wang, Yupeng. "Analysis of financial business model towards big data and its applications." Journal of Visual Communication and Image Representation 71 (August 2020): 102729. http://dx.doi.org/10.1016/j.jvcir.2019.102729.
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Bao, Xiuwen. "Construction of Financial Management System Model Based on Internet Technology." Wireless Communications and Mobile Computing 2022 (May 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/7487770.
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Based on the continuous development of Internet technology, the Internet has been fully applied in many fields. How can enterprises effectively use Internet technology to improve production efficiency, optimize the operating environment, and consolidate overall profitability? Combining the actual situation of enterprises with Internet technology has become one of the hot issues in the current society. With the increasing business volume of enterprises, the financial data generated by the data management system of enterprise financial department has become increasingly huge. At the same time, a large amount of financial data plays a vital role in the development of enterprises. In this paper, two data processing technologies, big data technology and data mining technology, are adopted to design and optimize the enterprise financial management model under the framework of Internet. It is very important and urgent to use data mining technology to realize the systematic management of enterprise finance, and it is also of practical value to study huge enterprise financial data based on data mining. In order to maintain sustainable development in the fierce market competition, enterprises must change the existing financial analysis work mode. Based on the theory of enterprise financial management under Internet information, this paper compares the factors affecting the financial management mode. Therefore, it is necessary to design the financial management model of enterprises.
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Yu, Yong, and Xiaoguo Yin. "Financial Risk Avoidance Based on the Sensor Network and Edge Computing." Journal of Electrical and Computer Engineering 2022 (May 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/2028155.
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In order to improve the effect of financial risk aversion, this paper studies the financial risk aversion system combined with the edge computing method of the sensor network and proposes a sensor data anomaly detection algorithm based on the offset distance. Moreover, this paper divides the sensor data into several sliding windows according to the time series, analyzes the offset between the data object and other data in the sliding window by calculating the offset distance, and uses the abnormal level to indicate the possibility of data abnormality. In addition, this paper analyzes the single-layer linear network model for the data with high abnormal level and constructs a financial risk aversion model based on the edge computing of the sensor network. The simulation test results show that the financial risk aversion model based on sensor network and edge computing proposed in this paper meets the actual needs of financial risk analysis.
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Habtor, Saleh Abdulaziz, and Ahmed Haidarah Hasan Dahah. "Machine-Learning Classifiers for Malware Detection Using Data Features." Journal of ICT Research and Applications 15, no. 3 (December 28, 2021): 265–90. http://dx.doi.org/10.5614/itbj.ict.res.appl.2021.15.3.5.
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The spread of ransomware has risen exponentially over the past decade, causing huge financial damage to multiple organizations. Various anti-ransomware firms have suggested methods for preventing malware threats. The growing pace, scale and sophistication of malware provide the anti-malware industry with more challenges. Recent literature indicates that academics and anti-virus organizations have begun to use artificial learning as well as fundamental modeling techniques for the research and identification of malware. Orthodox signature-based anti-virus programs struggle to identify unfamiliar malware and track new forms of malware. In this study, a malware evaluation framework focused on machine learning was adopted that consists of several modules: dataset compiling in two separate classes (malicious and benign software), file disassembly, data processing, decision making, and updated malware identification. The data processing module uses grey images, functions for importing and Opcode n-gram to remove malware functionality. The decision making module detects malware and recognizes suspected malware. Different classifiers were considered in the research methodology for the detection and classification of malware. Its effectiveness was validated on the basis of the accuracy of the complete process.
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Hastuti, Indhi, and Totok Dewayanto. "FRAUD DIAMOND DAN KECURANGAN PELAPORAN KEUANGAN PADA SAAT SEBELUM DAN SAAT COVID-19 DENGAN GOOD CORPORATE GOVERNANCE SEBAGAI VARIABEL MODERATING." Jurnal Ilmu Manajemen dan Akuntansi Terapan (JIMAT) 13, no. 2 (December 9, 2022): 58. http://dx.doi.org/10.36694/jimat.v13i2.428.
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The phenomenon that is currently happening is covid-19, in this research the researcher wanted to know before and during covid-19 whether there was fraud in financial reporting by using fraud diamonds to detect the fraud. The sample used is a manufacturing company with a period of 2018 - 2020.This study uses the independent variable external pressure for DAR proxy, financial target for ROA proxy, nature of industry for Inventory proxy, change in auditor and change in director. Good corporate governance is also used in this study as a moderating variable. The data processing used by the researcher is SPSS version 20.0. The results of this study indicate that the independent variable external pressure has an influence on fraudulent financial reporting either before or during covid-19 and also when using moderating variables.
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Honchar, Liubov, Anzhela Malakhova, and Olha Nevkypila. "FINANCIAL FRAUD AND SECURITY." INNOVATIVE ECONOMY, no. 3-4 (2021): 170–74. http://dx.doi.org/10.37332/2309-1533.2021.3-4.24.
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Purpose. The aim of the study is to characterize the features of financial fraud in enterprises and in people’s lives, to identify identification criteria and substantiate practical recommendations for ways to minimize it in modern conditions. Methodology of research. The methodological basis of the study was scientific methods of cognition, based on a systematic approach to solving problems. A number of general and special scientific research methods were used to achieve this goal and obtain research results: dialectical; monographic (when processing scientific publications); system analysis (in the study of types of financial fraud and security of the population); method of analogies and comparisons (in the study of international practice for the prevention of financial fraud); comprehensive analysis (in the formation of conclusions and proposals for solving the tasks) and others. Findings. The article reveals the problems of financial fraud in the current conditions of instability of Ukraine’s economy. Based on the analysis of theoretical and methodological approaches, the essence of the category "fraud" is revealed. Peculiarities of manifestation of different types of financial fraud at enterprises are given, such as: embezzlement or theft by an employee; fraud by managers or managers; investment scams; fraud on the part of suppliers; fraud on the part of the customer or client. The main methods of counteracting financial fraud at enterprises are revealed, in particular: unexpected inspection of the enterprise; legendary check; internal investigation of fraud; obtaining information from open sources. A study of the main modern types of financial fraud against individuals, in particular: social engineering; streaming; phishing; theft of a financial phone number; financial pyramids. The statistical indicators of the negative consequences of the activities of financial fraudsters in Ukraine are given. The directions of avoiding the negative consequences of illegal actions are determined, such as: non-dissemination of one’s personal data; non-disclosure of bank card information, passwords from private accounts on the bank’s website; not opening letters in the mail from suspicious addresses; careful inspection of the ATM before its use; cover the keyboard when entering the PIN code of the bank card; do not install suspicious programs; not to believe offers with earnings of super profits, etc. Originality. For the first time, a comprehensive analysis of the manifestation of various types of financial fraud was carried out and practical recommendations were substantiated on the directions of their minimization, taking into account the best foreign experience. Practical value. Proposed recommendations for combating financial fraud based on the results of the study will help to increase the level of financial security of the population of Ukraine. Key words: financial fraud, financial security, social engineering, streaming, phishing, financial pyramids.
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Lukić, Radojko. "Evaluation of financial performance and efficiency of companies in Serbia." Journal of Engineering Management and Competitiveness 12, no. 2 (2022): 132–41. http://dx.doi.org/10.5937/jemc2202132l.
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Recently, various methods of multi-criteria decision-making, as well as DEA(Data Envelopment Analysis) models, have been used more and more worldwide to measure the financial performance and efficiency of companies. Based on that, this paper analyzes the efficiency of companies in Serbia using the ARAS method. According to the ARAS method, five most efficient companies in Serbia are JP POŠTA SRBIJE Belgrade, JP EPS Belgrade, JP SRBIJAGAS NOVI SAD, JP PUTEVI SRBIJE Belgrade and COCA-COLA HBC - Serbia DOO ZEMUN. First four are public companies, and the fifth is from the processing industry sector. Public enterprises are fundamentally efficient. Trading companies are well positioned. So, for example, the DELHAIZE Serbia DOO Belgrade retail chain is in the eleventh place. The efficiency factors of companies in Serbia are, in addition to macroeconomics, managerial skills in managing the company. They differ from company to company. Digitization of the company's entire operations plays a significant role in this.
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Anak Agung Ayu Agung Cleo Bayu Pertiwi, I Nyoman Putu Budiartha, and Desak Gde Dwi Arini. "Pelaksanaan Perjanjian Sewa Beli Kendaraan Bermotor Akibat Overmacht Karena Covjd-19 di PT. Federal International Finance(FIF) Kabupaten Karangasem." Jurnal Interpretasi Hukum 2, no. 2 (June 17, 2021): 223–28. http://dx.doi.org/10.22225/juinhum.2.2.3393.223-228.
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The financial sector in Indonesia is a sector that is most important than advancing and developing the level of the economy. This of course needs to be maintained both by banks and other financial institutions so that the process of economic development in Indonesia is not delayed. Moreover to maintain this thing, it is necessary to make improvements which in particular do it with financial institutions and of course not banks. The purpose of this study is to reveal the factors that cause overmacht due to Covid-19 at PT. Federal International Finance (FIF) Karangasem Regency and efforts to resolve the overmacht due to Covid-19 at PT. Federal International Finance (FIF) Karangasem Regency. This research method using empirical legal research with a literature study approach. The sources of data used are primary data and secondary data. Data collection techniques by observing, interviewing and documentation methods. After primary legal data and secondary legal data are collected, the data will then be processed and analyzed using systematic legal data processing methods. The research results reveal that some of the problems identified lie in internal and external factors where these problems cannot be borne by the consumer and beyond the control of the consumer himself. The overmatch settlement is carried out by using non-litigation.
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Priatna, Apit, and Arif Maulana Yusuf. "PERANCANGAN SISTEM INFORMASI PENGELOLAAN KEUANGAN SEKOLAH BERBASIS WEB DAN SMS GATEWAY DI SMK JAYABEKA 02 KARAWANG." INTECH 2, no. 2 (December 5, 2021): 7–12. http://dx.doi.org/10.54895/intech.v2i2.1013.
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SMK Jayabeka 02 is a private school located in the Karawang area, which has 4 expertise programs, namely mechanical engineering, light vehicle automotive engineering, office administration and computer network engineering. School financial management still uses Microsoft Excel which requires accuracy in managing and recording, then the data processing process requires a lot of time and energy if the data is already queuing a lot. The purpose of this study is to facilitate the administrative transactions of school payments and expenses as well as to produce accurate data and increase the confidence of parents in students and schools. The method used is observation, interviews and literature study to obtain the required data and use SDLC for the system development model described by the waterfall. With the creation of the system, it is hoped that it can further facilitate every existing process and can improve the quality and trust of parents of students towards students and the school.
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Wang, RenLan, and Yanhong Wu. "Application of Blockchain Technology in Supply Chain Finance of Beibu Gulf Region." Mathematical Problems in Engineering 2021 (March 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/5556424.
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Blockchain technology is a database that is operated by multiple parts and forms a chain structure through hash index. The blockchain uses multiple nodes and distributes multiple accesses to data, thereby reducing the dependence on the central Internet server and avoiding the possibility of damage to the central server point due to data and data loss. Encryption technology is used to ensure its integrity and ensure that the data files stored in the blockchain are not tampered with or deleted maliciously. Blockchain technology has inherent advantages in supply chain finance with its technical attributes such as nontampering, distributed ledger, and traceability and has great potential to build trust to solve the main problems of supply chain finance, which is conducive to promoting financial development in the Beibu Gulf region. This article mainly introduces the application research of blockchain technology in supply chain finance in the Beibu Gulf region and intends to provide some ideas for the development of supply chain finance in the Beibu Gulf region combined with blockchain technology. This article proposes the application research methods of blockchain technology in supply chain finance in the Beibu Gulf region, including blockchain technology, supply chain financial risk evaluation on the blockchain, and supply chain finance game for relevant experiments. The experimental results of this article show that the average processing time of the algorithm of the designed blockchain supply chain financial system is 4.10 seconds, the algorithm processing efficiency is faster, and the relevant risks can be better assessed.
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LIU, X. H., E. X. WANG, and Y. Q. ZHENG. "RANDOM FOREST ALGORITHM OPTIMIZATION OF ENTERPRISE FINANCIAL INFORMATION MANAGEMENT SYSTEM." Latin American Applied Research - An international journal 48, no. 4 (October 31, 2018): 255–60. http://dx.doi.org/10.52292/j.laar.2018.237.
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The optimization of random forest algorithms for enterprise financial information management systems is studied in this paper. A random forest algorithm was proposed to improve the data processing capabilities of the financial system. This paper proposes a random forest model on the premise of referring to the latest results of machine learning. The algorithm was introduced into the real estate business financial management system in this paper. First, the samples are divided into training samples and test samples, and the direct prediction method and the two-step prediction method are applied. Mean SR and MAPE were used to compare the prediction accuracy of different algorithms and it was found that the direct prediction method is better. In the algorithm used in this paper, the random forest effect is the best. Then the linear regression, decision tree, neural network and random forest model fitting effects were compared and the best fitting degree of random forest was found.
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Albahli, Saleh, Aun Irtaza, Tahira Nazir, Awais Mehmood, Ali Alkhalifah, and Waleed Albattah. "A Machine Learning Method for Prediction of Stock Market Using Real-Time Twitter Data." Electronics 11, no. 20 (October 21, 2022): 3414. http://dx.doi.org/10.3390/electronics11203414.
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Finances represent one of the key requirements to perform any useful activity for humanity. Financial markets, e.g., stock markets, forex, and mercantile exchanges, etc., provide the opportunity to anyone to invest and generate finances. However, to reap maximum benefits from these financial markets, effective decision making is required to identify the trade directions, e.g., going long/short by analyzing all the influential factors, e.g., price action, economic policies, and supply/demand estimation, in a timely manner. In this regard, analysis of the financial news and Twitter posts plays a significant role to predict the future behavior of financial markets, public sentiment estimation, and systematic/idiosyncratic risk estimation. In this paper, our proposed work aims to analyze the Twitter posts and Google Finance data to predict the future behavior of the stock markets (one of the key financial markets) in a particular time frame, i.e., hourly, daily, weekly, etc., through a novel StockSentiWordNet (SSWN) model. The proposed SSWN model extends the standard opinion lexicon named SentiWordNet (SWN) through the terms specifically related to the stock markets to train extreme learning machine (ELM) and recurrent neural network (RNN) for stock price prediction. The experiments are performed on two datasets, i.e., Sentiment140 and Twitter datasets, and achieved the accuracy value of 86.06%. Findings show that our work outperforms the state-of-the-art approaches with respect to overall accuracy. In future, we plan to enhance the capability of our method by adding other popular social media, e.g., Facebook and Google News etc.
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Mai, Weiming, and Raymond S. T. Lee. "An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis." Applied Sciences 11, no. 9 (April 25, 2021): 3876. http://dx.doi.org/10.3390/app11093876.
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Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.
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Isyandi, B., and R. Agus Trihatmoko. "An Analysis of Regional Economic Performance of Riau on the Capital Expenditure Budget: A Study of Indonesian Territorial Economics." International Journal of Public Policy and Administration Research 9, no. 2 (June 10, 2022): 33–45. http://dx.doi.org/10.18488/74.v9i2.3024.
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This study aims to assess the economic performance of Riau Province, Indonesia, both within the region and on a national scale. The research methodology used in this study is a descriptive statistical approach. The results of quantitative data processing are interpreted in an effort to develop the macroeconomic theories and frameworks relating to financial governance and regional budgets. The results of the study conclude that the economic performance of Riau has experienced a downward trend in recent years. In 2017, it reached IDR 471.42 trillion and grew about 2.71%, measured using Gross Regional Domestic Product (PDRB) at constant 2010 prices. In 2017, the Regional Expenditure Budget experienced a decline compared to 2016, in which it was IDR 35.44 trillion. Based on data analysis and interpretation, we propose frameworks for regional economic performance and the regional financial capability theory.
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Auber--Le Saux, Jessica, Vincent Detalle, Xueshi Bai, Michalis Andrianakis, Nicolas Wilkie-Chancellier, and Vivi Tornari. "Surface Displacement Measurements of Artworks: New Data Processing for Speckle Pattern Interferometry." Applied Sciences 12, no. 23 (November 23, 2022): 11969. http://dx.doi.org/10.3390/app122311969.
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Curators have developed preventive conservation strategies and usually try to control the temperature (T) and relative humidity (RH) variations in the museum rooms to stabilise the artworks. The control systems chosen by museums depend on the size and age of the building, the financial means and the strategies that can be adapted. However, there is a lack of methods that can monitor mechanical changes or chemical reactions of objects in real-time or regularly. It would therefore ideally be preferable to monitor each of them to alert them to preserve them. For this purpose, a non-destructive, non-contact, full-field technique, Digital Holographic Speckle Pattern Interferometry (DHSPI), has already been developed and allows direct tracking of changes on the surface of artworks. This technique is based on phase-shifting speckle interferometry and gives the deformation of the surface below the level of the micro-meter of the analysed object. In order to monitor the deformation continuously, a large number of images are acquired by DHSPI and have to be processed. The existing process consists of removing noise from the interferogram, unwrapping this image, and deriving and displaying a 2D or 3D deformation map. In order to improve the time and accuracy of processing the imaging data, a simpler and faster processing method is developed. Using Matlab®, a denoising methodology for the interference pattern generated during data acquisition is created, based on a stationary wavelet transform. The unwrapped image is calculated using the CPULSI (Calibrated Phase Unwrapping based on Least-Squares and Iterations) algorithm as it gives the fastest results among the tested methods. The unwrapped phase is then transformed into surface displacement. This process performs these steps for each interferogram automatically. It allows access to 2D or 3D deformation maps.
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Carvalho, Pedro, Américo Pereira, and Paula Viana. "Automatic TV Logo Identification for Advertisement Detection without Prior Data." Applied Sciences 11, no. 16 (August 15, 2021): 7494. http://dx.doi.org/10.3390/app11167494.
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Advertisements are often inserted in multimedia content, and this is particularly relevant in TV broadcasting as they have a key financial role. In this context, the flexible and efficient processing of TV content to identify advertisement segments is highly desirable as it can benefit different actors, including the broadcaster, the contracting company, and the end user. In this context, detecting the presence of the channel logo has been seen in the state-of-the-art as a good indicator. However, the difficulty of this challenging process increases as less prior data is available to help reduce uncertainty. As a result, the literature proposals that achieve the best results typically rely on prior knowledge or pre-existent databases. This paper proposes a flexible method for processing TV broadcasting content aiming at detecting channel logos, and consequently advertising segments, without using prior data about the channel or content. The final goal is to enable stream segmentation identifying advertisement slices. The proposed method was assessed over available state-of-the-art datasets as well as additional and more challenging stream captures. Results show that the proposed method surpasses the state-of-the-art.