Intelligent Data Processing Research Articles

ИНТЕЛЛЕКТУАЛЬНАЯ ОБРАБОТКА БОЛЬШИХ ДАННЫХ В ЗАДАЧАХ МАЛОГО БИЗНЕСА НА ОСНОВЕ БАЙЕСОВСКИХ ИНТЕЛЛЕКТУАЛЬНЫХ ТЕХНОЛОГИЙ

The article is devoted to solving the urgent problem of creating intelligent big data processing systems for small businesses. The specificity of information flows of small businesses, in particular in the service sector, is shown.The main tasks of increasing the efficiency of small businesses based on digitalization are identified. The methodology of the regularizing Bayesian approach (RBP), Bayesian intelligent measurement technologies (BII) and a digital platform for managing small businesses and creating monitoring and decision support systems are proposed. Based on this platform of the RBP methodology, a working sample of a complex for service enterprises – fitness centers has been developed. The unique properties of an intelligent complex are the possibilities of integrating different types of information, intelligent processing of large data streams in the form of numerical and linguistic information under conditions of considerable uncertainty In the software package for the service sector, various situations can be simulated when working with clients, as well as digital images of clients can be created to personalize work with each of them for the organization of effective and profitable work of small business companies.

Distributed Intelligent Transportation System Data Management and Processing Platform

In recent years, with the rapid development of China's road construction and the continuous improvement of people's living standards, the number of urban motor vehicles has continued to grow year after year. Motor vehicles have become one of the main means of transportation for urban residents. At the same time, due to the increasing amount of traffic information data, a single server platform can not meet the reception and storage of massive traffic data, nor can it meet the needs of rapid query of massive traffic data. Therefore, this paper will use the distributed system to process data to solve the above problems, so as to ensure the real-time transmission and storage of massive traffic data. This paper discusses and analyzes the characteristics of distributed intelligent transportation system and the design of communication system data management and processing platform. Through the behavior decision algorithm of distributed transportation system, the flow data of distributed intelligent transportation system are tested and analyzed. The experimental results show that in the time period from 6:00 to 8:00, from 12:00 to 14:00 and from 16:00 to 18:00 when the traffic data reaches the peak, the amount of data processed by each communication server is very different, and the difference data amount can reach 60000 traffic data. The statistical results of this traffic data amount can show that the ratio of the data amount sent by each front-end bayonet every moment is not equal to the ratio of the data amount of each front-end bayonet every day, which is enough to show that the data management and processing capacity and efficiency of the distributed intelligent transportation system have been greatly improved.

Introduction to the Special Issue on “Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce”

Introduction to the Special Issue on “Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce”

Application of artificial intelligence and machine learning techniques in supporting the diagnosis and treatment of neurological diseases

The first two decades of the 21st century have seen great advances in artificial intelligence and machine learning. The techniques have found their way into everyday life, for example in smartphones, search engines, digital customer assistants, motion control systems, and biomedical devices. The aims of this paper are to outline the possibilities for using artificial intelligence and machine learning techniques in supporting the diagnosis and treatment of neurological diseases, and to discuss selected applications of these techniques based on the most recent published reports. First, contemporary definitions of artificial intelligence and machine learning are presented. This is followed by a review of the most important techniques for intelligent data processing: search methods, mathematical logic, probabilistic methods, classifiers, and artificial neural networks (including deep and convolutional networks). Areas of application of these techniques in medicine are identified, including disease diagnosis and support of treatment as well as monitoring and prediction of changes in health status. The role of artificial intelligence and machine learning in neuroscience is presented, together with examples of diagnostic applications based on anatomical, morphological and functional brain connectivity data. Sample applications of intelligent techniques in supporting the treatment (including surgical management) of nervous system diseases are also described. Ambient smart devices monitoring the health status of patients with chronic neurological conditions are discussed, and selected projects based on smart techniques to support early detection of symptoms of neurodegenerative disorders are described. The conclusions highlight the potential of the techniques, as well as the challenges and risks associated with them. A possible synergy between intelligent systems and actions taken by medical staff is outlined as a way to improve the safety and quality of life of patients with acute and chronic neurological diseases.

Application of Data Mining in the Evaluation of Enterprise Lean Management Effect

In order to improve the effect of enterprise lean management, this study proposes a lean data mining algorithm based on the characteristics of lean data in enterprise management. This study connects data mining and lean production to study the data of enterprise management operation, proposes an intelligent data processing model suitable for modern enterprise management, and constructs the model function module in combination with the enterprise operation management process. Moreover, this study constructs an evaluation system for the effect of enterprise lean management based on data mining. The system provides a human-computer interaction interface, and operators can use various functions and services provided by the system through a visual interface. Through experimental research, it can be known that the enterprise lean effect evaluation system based on data mining proposed in this study can play an important role in enterprise lean management.

A Data Technology Oriented to Information Fusion to Build an Intelligent Accounting Computerized Model

The development of management accounting promotes the integration of business and finance, and with the continuous development of information technology and the advent of the era of big data, the development of corporate financial informatization provides tools for the integration of business and finance. This paper improves the big data technology, improves the traditional accounting process, combines the big data technology to build a computerized accounting system, and obtains scientific and effective accounting information processing results through intelligent big data processing. The design goal of the enterprise accounting management system is that the system can efficiently complete the enterprise cost budget accounting work after the design is completed and ensure the normal and stable progress of the enterprise cost budget accounting work. Through the experimental research results, it can be known that the computerized intelligent accounting system based on big data technology constructed in this article has certain effects.

Rapid characterization of the constituents in Jigucao capsule using ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry.

Jigucao capsuleis a well-known Chinese patentmedicine for the treatment of acute and chronic hepatitis and cholecystitis. The chemical components of Jigucao capsule were not clear resulting from the paucity of relevant studies, which hindered the research of the pharmacological mechanism, the comprehensive development, and utilization of Jigucao capsule in clinical studies. By establishing a high-throughput ultra-performance liquid chromatography quadrupole time of flight mass spectrometry in combination with intelligent UNIFI software data processing platform to automatically characterize and identify the chemical profile of Jigucao capsule, 144 compounds were determined rapidly, including 34 terpenoids, 25 flavonoids, 22 steroids, 21 phenylpropanoids, 10 glycosides, six alkaloids, 13 organic acids, and other 13 components. These compounds may be the active components of Jigucao capsule. In this study, a rapid and robust method for comprehensively analyzing the chemical composition of Jigucao capsule was described and established for the first time. The results will provide a reference for the quality control of Jigucao capsule and the establishment of a higher quality standard, as well as for the pharmacodynamic material basis research.

Data Mining Technology Used in an Internet of Things-Based Decision Support System for Information Processing Intelligent Manufacturing

In recent years, database technology has improved significantly, and database management systems have gained widespread adoption. As a result, the volume of data saved across numerous databases has increased exponentially. However, the vast majority of information is hidden beneath this mountain of data. The goal of this study is to get a comprehensive understanding of the decision information system employed in the Internet of Things for intelligent manufacturing data processing. The proposed Decision support system (DSS) information processing is accomplished through the use of an IoT-based intelligent manufacturing data mining model. Numerous DM algorithms that are frequently encountered are analyzed, including the ARS and Apriori Algorithm (AA). The Decision Tree data mining algorithm is investigated, as is the generation of several Decision Trees and the pruning algorithm for digital twins. The findings demonstrate that data mining technology is capable of analyzing statistical data from a variety of angles and perspectives by modeling, classifying, and grouping large amounts of data as well as discovering correlations between them. Additionally, statistical work involves the calculation of data and the use of their correlations to aid in decision analysis. The proposed theoretical framework demonstrates how DSS-integrated components can work cooperatively in Intelligent Manufacturing to define a stable data flow within the Internet of Things. Particular emphasis is placed on conceptualizing the decision support system's integrated performance.

Requirements for the Procedure for Assessing the Operator’s Person When Processing Accelerometer Data

The article considers the possibility of biometric authentication based on gait parameters, which are obtained after intelligent processing of the accelerometer data of a wearable device. The article discusses the main trends and trends in the field of biometric authentication, as well as authentication by gait parameters. The developed neural network algorithm and informative parameters are described in the authentication procedure based on the data of a single sensor of a portable device. The practical verification of the proposed approach is carried out on 32 subjects of different physiology. The results of the study show the possibility of distinguishing their own movements in 100% of cases, and the distinction of the subjects is more than 90%. Also, the final part of the article provides the requirements for the authentication procedure when processing accelerometric data of gait biometrics, the level of trust of the developed algorithm is determined.

Disease risk analysis for schizophrenia patients by an automatic AHP framework

BackgroundBased on more than 15 million follow-up records of 404,426 patients from Guangdong Mental Health Center over the past 10 years, this study aims to propose a disease risk analysis and prediction model to support chronic disease management and clinical research for schizophrenia patients.MethodsBased on a mental health information and intelligent data processing platform, we design an automatic AHP framework called AutoAHP to analyze and predict the disease risks of schizophrenia patients. Through automatic extraction, transformation and integration of follow-up data in the real world such as demography, treatment, and the disease course, a chronic database of patient status is established. In combination with age-period-cohort, logistic regression and Cox models, we apply the AutoAHP to assess disease risk and implement risk prediction in practice.ResultsA list of essential factors for risk prediction are identified, including annual changes in mental health policy, public support, regional difference, patient gender, compliance, and social function. After the verification of 1,222,038 complete disease course and treatment records of 256,050 patients, the AutoAHP framework achieves a precision of 0.923, a recall of 0.924, and a F1 of 0.923. The model is demonstrated to be superior to general models and has better performance in risk prediction.ConclusionsAiming at the risk assessment of patients with schizophrenia which is influenced by factors, such as time, region and complication, the AutoAHP framework is able to be applied as a model in combination with logistic regression and Cox models to support clinical analysis of disease risk related factors and assist decision-making in chronic disease management.

Towards fully verifiable forward secure privacy preserving keyword search for IoT outsourced data

As cloud computing and Internet of Things (IoT) are becoming more and more deeply integrated, the new secure challenge for IoT data has attracted great attention. Privacy preserving keyword search, as one important secure technique, achieves search over encrypted cloud-based IoT data. Forward security and verifiability are two important security properties for privacy preserving keyword search for cloud-based IoT data. Forward security makes privacy preserving keyword search schemes for cloud-based IoT data able to resist file injection attack, which is one new attack on privacy preserving keyword search when IoT data is updated. Verifiability makes privacy preserving keyword search schemes for cloud-based IoT data able to ensure the validity of the search results returned by cloud. However, to the best of our knowledge, all of existing privacy preserving keyword search schemes at most achieve forward security and partial verifiability. In order to solve this problem, we explore how to simultaneously achieve the forward security and the full verification for intelligent encrypted data processing in cloud-based IoT and propose the first fully verifiable forward secure privacy preserving keyword search scheme for IoT outsourced data. In order to achieve forward security, we construct the secure index based on the hash chain. In each update, the IoT data owner randomly chooses a string as a new state to generate the new trapdoor. The new trapdoor cannot match previous trapdoors. In order to achieve the full verifiability while still maintaining forward security for intelligent encrypted data processing in cloud-based IoT, we design novel authentication construction called Forward Secure Accumulative Authentication Tag (FSAAT). This kind of tag has the incremental property and supports the verification on the basis of efficient data update. We prove that our scheme is secure and efficient through detailed security analysis and experiments.

Opening Remarks by the Program Committee of the Conference “Intelligent Data Processing. Theory and Applications” (IDP-2020)

Opening Remarks by the Program Committee of the Conference “Intelligent Data Processing. Theory and Applications” (IDP-2020)

Novel Computation and Communication Resources Allocation Using Relay Communications in UAV-Mounted Cloudlet Systems

At present, the Internet of things (IoT) is widely employed in various systems. The operation of these systems requires intelligent data processing, which needs to be performed rapidly at the network edge. Hence, studies on mobile edge computing (MEC) have been increasingly conducted in recent years. In MEC, edge servers perform data processing to reduce service delay. To meet the demand for computational and communication resources in infrastructureless areas or during large-scale disasters, the use of unmanned aerial vehicles (UAVs) in providing computational resources has attracted considerable attention. However, in MEC using UAVs, it is necessary to consider the workload balance and communication range of vehicles used. In this study, we highlight problems associated with such systems from a new perspective. Furthermore, we propose a relay control method equalizing the workload balance between UAVs and reducing network delay. We modeled the proposed method as a complex system, mathematically formulating each delay element. Additionally, experimental results highlight the effectiveness of the proposed method in various environments. The reduction in delay considerably improved the feasibility of the system. This study is expected to help achieve a significant advancement in MEC using UAVs by establishing a foundation for further research.

Shelf-Life Prediction of Glazed Large Yellow Croaker (Pseudosciaena crocea) during Frozen Storage Based on Arrhenius Model and Long-Short-Term Memory Neural Networks Model

In this study, the changes in centrifugal loss, TVB-N, K-value, whiteness and sensory evaluation of glazed large yellow croaker were analyzed at −10, −20, −30 and −40 °C storage. The Arrhenius prediction model and long-short-term memory neural networks (LSTM-NN) prediction model were developed to predict the shelf-life of the glazed large yellow croaker. The results showed that the quality of glazed large yellow croaker gradually decreased with the extension of frozen storage time, and the decrease in quality slowed down at lower temperatures. Both the Arrhenius model and the LSTM-NN prediction model were good tools for predicting the shelf-life of glazed large yellow croaker. However, for the relative error, the prediction accuracy of LSTM-NN (with a mean value of 7.78%) was higher than that of Arrhenius model (with a mean value of 11.90%). Moreover, the LSTM-NN model had a more intelligent, convenient and fast data processing capability, so the new LSTM-NN model provided a better choice for predicting the shelf-life of glazed large yellow croaker.

Intelligent data processing methods in sensor networks of mobile and autonomous objects

In the near term, tasks related to monitoring mobile geographically distributed objects with the aim of efficiently managing them are acquiring great importance. An example of such tasks is the monitoring the state of given number of vehicles of agricultural enterprise, when it becomes necessary to promptly transfer given amount of data with high requirements for the level of their reliability. The use of the infrastructure of cellular mobile networks for this is not suitable for a number of reasons for rural areas. The paper proposes an approach based on the use of high-speed dynamic sensor networks. This makes it possible to carry out relay transmission of data between objects, including mobile ones. However, the issues of building flexible data transfer protocols in networks of this kind, providing a significant data transfer rate and taking into account the priority of the transmitted information and its features, still remain open. At the same time, for mobile objects in conditions of non-stationarity of the parameters of communication channels, the problems of ensuring the required reliability of data are of particular importance. To solve the last problem, it is proposed to use the permutation decoding method based on the cognitive metaphor.

Preparation of Innovative Development Rating of Russian Regions by the Level of University Involvement

The paper presents method of clustering and grouping the university performance indicators to prepare an integral rating of Russian regions by the level of university involvement in innovative development of Russian regions. The following problems that require the management influence of state structures are considered: the role of the research base of regional universities in strengthening the innovative potential of regions, the degree of involvement of universities in the innovative regional space.The aim of the study is to develop a rating system for Russian regions according to the degree of university involvement in innovative development using mathematical tools and an intelligent data processing system. The main hypothesis of the article is the existence of a link between regional innovative development and the effectiveness of university contributions. The mathematical approach involves the consideration and multidimensional ranking of the main groups of university performance indicators in order to obtain cluster classifications within each of the problems posed. The solution to the problems is to build a barometer of innovation activity in the form of a multi-purpose problem-oriented rating.Materials and methods. An approach is applied that includes the assessment and multidimensional ranking of the contribution of universities using groups of integral indices created on the basis of aggregation of several important indicators of the university’s activity. Computational experiments were conducted according to the data of the Ministry of Education and Science for the regions of Russia and regional universities. 16 indicators of Russian regions for 2016 are considered.Results and discussion. The ranking and comparison of universities according to the degree of involvement in the innovative development of the region was performed using an indicator model and aggregation depending on the target problem. Quantitative indicators of the quality of university’s activities by regions are obtained, which allow us to obtain an integral rating of Russian regions by the level of university involvement. Unlike other approaches, the author’s method includes three components, which are independent integral indexes and indicate the level of involvement of universities in regional innovative development. Statistical data on the leading indicators of Russian universities for 2016 were processed. The methodology Science-model with three factors - the model with regrouping, named by the author A-B-C, showed the high potential of Russian universities to balance their regional demand as management research centers.Conclusion. The results of the study are compared with the ratings of the well-known agencies. The author hopes that soon Russia will have a reliable scientometric system at the level of rating universities on their involvement in the innovative development of Russia, such a rating will be an indisputable argument in favor of financing regional universities. The author laid down a high requirement: compliance with the three models, only in this case, regional universities can receive funding from the municipality, after redistribution from the center. At the same time, it is necessary to carefully choose universities in which projects will receive development and perspective. Regional authorities must meet the requirements to receive the necessary investments in promising projects. The scientific potential and demand for theoretical research for their full application at all enterprises, the combination of theoretical science and practical implementation will reduce the cost of stabilizing outdated technologies in all areas of knowledge and use the experience of older generations and the strength of young people for high-tech production growth in Russia. Therefore, the results of the study will be useful to federal authorities and financial and credit organizations that provide financing.

Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector

The organized large-scale retail sector has been gradually establishing itself around the world, and has increased activities exponentially in the pandemic period. This modern sales system uses Data Mining technologies processing precious information to increase profit. In this direction, the extreme gradient boosting (XGBoost) algorithm was applied in an industrial project as a supervised learning algorithm to predict product sales including promotion condition and a multiparametric analysis. The implemented XGBoost model was trained and tested by the use of the Augmented Data (AD) technique in the event that the available data are not sufficient to achieve the desired accuracy, as for many practical cases of artificial intelligence data processing, where a large dataset is not available. The prediction was applied to a grid of segmented customers by allowing personalized services according to their purchasing behavior. The AD technique conferred a good accuracy if compared with results adopting the initial dataset with few records. An improvement of the prediction error, such as the Root Mean Square Error (RMSE) and Mean Square Error (MSE), which decreases by about an order of magnitude, was achieved. The AD technique formulated for large-scale retail sector also represents a good way to calibrate the training model.

Geometric Feature Extraction of Point Cloud of Chemical Reactor Based on Dynamic Graph Convolution Neural Network.

Geometric features are an important factor for the classification of drugs and other transport objects in chemical reactors. The moving speed of drugs and other transport objects in chemical reactors is fast, and it is difficult to obtain their features by imaging and other methods. In order to avoid the mistaken and missed distribution of drugs and other objects, a method of extracting geometric features of the drug’s point cloud in a chemical reactor based on a dynamic graph convolution neural network (DGCNN) is proposed. In this study, we first use MATLAB R2019a to add a random number of noise points in each point cloud file and label the point cloud. Second, k-nearest neighbor (KNN) is used to construct the adjacency relationship of all nodes, and the effect of DGCNN under different k values and the confusion matrix under the optimal k value are analyzed. Finally, we compare the effect of DGCNN with PointNet and PointNet++. The experimental results show that when k is 20, the accuracy, precision, recall, and F1 score of DGCNN are higher than those of other k values, while the training time is much shorter than that of k = 25, 30, and 35; in addition, the effect of DGCNN in extracting geometric features of the point cloud is better than that of PointNet and PointNet++. The results show that it is feasible to use DGCNN to analyze the geometric characteristics of drug point clouds in a chemical reactor. This study fills the gap of the end-to-end extraction method for a point cloud’s corresponding geometric features without a data set. In addition, this study promotes the institutionalization, standardization, and intelligent design of safe production and management of drugs and other objects in the chemical reactor, and it has positive significance for the production cost and resource utilization of the whole pharmaceutical process. At the same time, it provides a new method for the intelligent processing of point cloud data.

Fuzzy Cloud Evaluation of Service Quality Based on DP-FastText

This study proposes a service quality evaluation model framework which integrates automatic data acquisition, intelligent data processing and real-time data analysis with online comment data as data sources by introducing natural language processing technology based on management methods to break the traditional idea of over-reliance on human resources for service quality evaluation. The framework is mainly divided into text data preparation, fine-grained sentiment analysis and fuzzy cloud evaluation models. Data preparation module is responsible for preparing the initial data, and the fine-grained sentiment analysis module is responsible for pre-training a fine-grained sentiment classification model. The fuzzy cloud evaluation module uses the data obtained from the first two modules to evaluate service quality. By applying the model into catering industry, the feasibility of the model is proved and individuality, efficiency, dynamicity and intelligence of the model give it more advantage in the practice of service quality evaluation

Preface

Due to recent pandemic, the 2021 International Conference on Big Data and Intelligent Algorithms (BDIA 2021) which was planned to be held in Chongqing, China was held virtually online during July 9-11, 2021. The decision to hold the virtual conference was made in compliance with many restrictions and regulations that were imposed by countries around the globe. Such restrictions were made to minimize the risk of people contracting or spreading the COVID-19 through physical contact. There were 70 individuals who attended this online conference, represented many countries including Tunisia, Iran, Spain and China.BDIA 2021 dedicates to create a platform for academic communications between specialists and scholars in the fields of Big Data and Intelligent Algorithms. The conference created a path to establish a research relation for the authors and listeners with opportunities for collaboration and networking among the universities and institutions for promoting research and developing technologies. BDIA 2021 welcomes all high-quality research papers and presentations from related research fields.During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. In the second part, we invited two professors as our keynote speakers. Prof. Zhikui Chen, from Dalian University of Technology, China, performed a speech: Multimodal data learning and applications. And then we invited Prof. Fengqing Han, from Chongqing Jiaotong University, China as our finale keynote speaker. He made a wonderful speech: The Asymmetric Evolutionary Game Based on Dynamic Payoff. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge.The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. Topics include but are not limited to the following areas: Big Data, Intelligent Data Processing, Algorithms, Nature Inspired Computing and Optimization, Intelligent Data Fusion. All the papers have been through rigorous review and process to meet the requirements of International publication standard.We would like to acknowledge all of those who supported BDIA 2021. The help and contribution of each individual and institution was instrumental in the success of the conference. In particular, we would like to thank the organizing committee for its valuable inputs in shaping the conference program and reviewing the submitted papers.We sincerely hope that the BDIA 2021 turned out to be a forum for excellent discussions that enabled new ideas to come about, promoting collaborative research. We are sure that the proceedings will serve as an important research source of references and knowledge, which will lead to not only scientific and engineering findings but also new products and technologies.Committee of BDIA 2021List of Committee member are available in this pdf