Intelligent Analysis Method Research Articles

Research on the Development Support Strategy of Cultural Enterprises Based on Fish Swarm Algorithm under the Background of Public Health.

Under the background of public health events, the government needs to adopt reasonable support strategies to help cultural enterprises tide over the crisis. However, the original analytic hierarchy process cannot be comprehensive and accurate analysis, resulting in that the government support strategy cannot play a role. Fish swarm algorithm is a comprehensive intelligent algorithm, which can comprehensively analyze the factors affecting the formulation of supporting strategies for cultural enterprises, and has the advantages of simple operation and strong analysis ability. Under this background, this paper puts forward a fish swarm model; this method is a comprehensive intelligent analysis method, which can help the government find the best support strategy from the perspectives of politics, economy, and society. In order to verify the effectiveness of the fish school model, this paper uses MATLAB software for verification; the results show that, in the support strategy classification, selection accuracy, and selection time, fish school model is superior to the analytic hierarchy process. Therefore, the fish swarm model can help the government to better choose supporting policies and help cultural enterprises tide over the crisis and improve the accuracy of formulating supporting strategies for cultural enterprises.

Intelligent Analysis Method of Artistic Environment Construction Style Based on Artificial Intelligence Technology

Environmental art mainly uses the collocation of color and spatial structure for landscape layout. Different environmental styles have different artistic characteristics. In order to better design the environmental space, this paper puts forward the intelligent analysis method of artistic environmental construction style based on artificial intelligence technology. By analyzing the environment into characteristics and the environmental style into process, the direction of environmental art design is innovated while the environmental design is carried out reasonably.

A new data mining method for time series in visual analysis of regional economy

In order to improve the effect of visual analysis of regional economy, this paper uses machine learning algorithms to analyze time series data, uses various models and methods of intelligent data analysis to mine data laws from huge data, statistical data reports, and find problems in economic development. Moreover, this paper combines the time series algorithm to design and plan the functional structure of the system, and design a separate module structure from the actual situation of regional economic analysis, and build a model system from the overall structure. After constructing the system, this paper tests the system. From the results of the experimental research, we can see that the regional economic visualization system based on time series constructed in this paper has perfect system functions and can meet the needs of regional economic analysis.

ENSEMBLE MODELS FOR INTELLIGENT ANALYSIS OF SOCIOLOGICAL DATA

The paper investigates an approach to sociological information processing based on the use of intelligent data analysis methods applied to the task of processing the results of a questionnaire survey. The advantages of intelligent analysis of sociological data in comparison with traditional statistical processing are discussed, as well as the implementation features and applicability limits of various intelligent data analysis methods in solving problems of association, clustering and classification. Structure and features of representation of respondents’ survey data are considered, the appropriateness is substantiated and the advantages of their processing based on the combination of various methods of intelligent analysis within an ensemble of models are discussed. A structure of an ensemble of models is proposed based on the combination and joint use of association rules, clustering algorithms and decision trees, which makes it possible to jointly process numerical and categorical data contained in the respondent’s answers to the questionnaire and also to interpret the results of data clustering. The paper describes the results of using the constructed ensemble of models for processing and analyzing the data of a sociological survey conducted as part of the annual project for monitoring the drug abuse situation in the Bryansk region in 2013 – 2018. The use of an ensemble of intelligent data analysis models for processing the results of a sociological survey not only makes it possible to detect patterns in them that cannot be otherwise detected by traditional methods of statistical processing, but also contributes to an increase in the reliability, completeness and coherence of the analysis results, due to which the analyst creates a holistic systemic picture of the studied social phenomenon or process.

A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances

AbstractIn general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. However, the development of Raman spectroscopy, machine learning, and deep learning techniques has paved the way for achieving efficient analytical tools capable of identifying mixture components, thus leading to an apparent breakthrough in the identification of mixtures beyond traditional chemical analysis methods. This review summarizes the work of Raman spectroscopy in identifying the composition of substances; reviews the preprocessing process of Raman spectroscopy, artificial intelligence analysis methods, and analysis procedures; and examines the application of artificial intelligence. Finally, the advantages and disadvantages and development prospects of Raman spectroscopy are discussed in detail.

Особенности статистического моделирования и прогнозирования преступности: теоретический аспект

The purpose of the work is to provide a theoretical analysis of modern methods of modeling and forecasting the state of crime, which can be used in the system of public administration of the law enforcement sphere. In the course of the research, the peculiarities of using various tools and models for predicting the state of crime are revealed. A significant part of the research of scientists is directed towards the use of spatial and spatiotemporal models, as well as methods of artificial intelligence. The high quality of monthly forecasts is noted. Various economic, social, geographical, temporal and other groups of factors that influence the state of crime are identified. It is established that the quality of the developed crime forecasts depends on the choice of the optimal method and period of forecasting, on the completeness of the information base, including social, economic, legal and other characteristics of the phenomena and processes of public life that affect the criminal situation. It is noted that the practical use of artificial intelligence and econometric analysis methods in predicting the state of crime is becoming particularly relevant at the present time.

Prematch Emotions and Coping Styles of Martial Arts Athletes Based on Artificial Intelligence

The fluctuation of martial arts athletes before competition is easy to affect martial arts competition. Emotional stimulation helps martial arts athletes to play in the competition field and improve their on-the-sport coping skills. In this paper, we use artificial intelligence technology to analyze the precompetition emotions of martial arts athletes, make guidance for the precompetition situation of martial arts athletes, and give to-the-spot response guidance and suggestions. In this paper, the artificial intelligence MLR (multiple logistic regression) combined with the unsupervised LOF (local outlier factor) algorithm is used to realize the precompetition emotion analysis and on-the-spot response guidance for martial arts athletes. In addition, this paper takes a community martial arts team as the test object and verifies the method through practice feedback. Studies have shown that this method of analysis and guidance can effectively assess the emotions of martial arts athletes and take intervention, compared to the absence of sentiment analysis and on-the-sport guidance for martial arts athletes. Compared to the coach’s intuitive guidance, the interventions in this treatise speed up 65%. From the experimental subjects in this paper, athletes of the same level provide emotional guidance and suggestions for responding to emotional fluctuations. This can psychologically improve the efficiency of athletes by more than 35%. Therefore, the use of artificial intelligence analysis methods can effectively improve the emotional stability of martial arts athletes before the game, so that they can deal with all kinds of emergencies on the spot.

Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP Algorithm

Under construction disturbance, the surrounding rock of a soft rock tunnel shows obvious aging characteristics. The creep characteristics of a rock mass under stress-seepage coupling greatly influence the long-term stability of a project. How to simply, quickly, and accurately determine the creep parameters of a rock mass under coupling conditions is significant to engineering structure design and construction. The optimal weights and thresholds of the BP neural network are sought through the immune algorithm to avoid the problem of slow convergence speed of the BP neural network and easy to fall into local optimum. Therefore, an intelligent back analysis method based on the IA-BP algorithm is established, which leads to the development of the corresponding intelligent back analysis program. The creep effect of the rock mass was simulated herein using the Drucker–Prager yield criterion and the time hardening creep law as the forward optimization method constitutive model. In addition, a sensitivity analysis of the parameters was performed to determine the optimal number of inversion parameters. By comparing and analyzing the residual between the inversion results of the IA-BP algorithm, PSO-BP algorithm, and the test values, the high precision of the IA-BP algorithm is proved. Taking the Lan Zhou-Hai Kou national expressway tunnel as an engineering example, a multiparameter creep inversion of the tunnel surrounding rock under the stress-seepage coupling condition was conducted using the inverse analysis method of the IA-BP algorithm. The results showed that the proposed IA-BP algorithm can effectively prevent the BP neural network from falling into a local minimum. Also, the algorithm is fast and accurate. The intelligent back analysis method based on the IA-BP algorithm is applied to the multifield coupling parameter back analysis, provides the basis and help for the structural design and construction of soft rock tunnel in water-rich stratum.

Evaluation of Deep Learning against Conventional Limit Equilibrium Methods for Slope Stability Analysis

This paper presents a comparison study between methods of deep learning as a new category of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, computer code was developed to calculate the factor of safety (FS) using four limit equilibrium methods: Bishop’s simplified method, the Fellenius method, Janbu’s simplified method, and Janbu’s corrected method. The code was verified against Slide2 in RocScience. Subsequently, the average FS values were used to approximate the “true” FS of the slopes for labeling the images for deep learning. Using this code, a comprehensive dataset of slope images with wide ranges of geometries and soil properties was created. The average FS values were used to label the images for implementing two deep learning models: a multiclass classification and a regression model. After training, the deep learning models were used to predict the FS of an independent set of slope images. Finally, the performance of the models was compared to that of the conventional methods. This study found that deep learning methods can reach accuracies as high as 99.71% while improving computational efficiency by more than 18 times compared with conventional methods.

Application of deep learning-based image recognition technology to asphalt–aggregate mixtures: Methodology

An important factor that affects the performance and durability of asphalt–aggregate mixtures is the size distribution of the aggregate particles. However, there are no accurate and quick methods of obtaining this information currently available. A potential solution to this problem is to use computer-aided image recognition technology, a technique that is being used increasingly more often in the field of pavement engineering. In this paper, asphalt core samples drilled from pavements are used as test objects. The information collected from the side surfaces of these cores is analyzed using neural network technology to help improve the speed and accuracy of the quantitative analysis of the asphalt mixtures. First, a camera is used to make a series of photographic images of the side surface and image reconstruction compositing is adopted to produce an image of the asphalt–aggregate mixtures. This image can be processed using a neural network (U-NET++) to identify the aggregate in the image. The training and recognition effect is improved by using Gaussian blur and secondary labeling methods. The grayscale images can then be binarized, filled, and the aggregate particles are segmented in order to identify holes and areas with adhesion problems. The geometric forms of the particles that appear on the side surfaces of the asphalt mixtures can be accurately obtained. Important geometric parameters (e.g. aspect ratio and roundness) can thus be derived from the image. A method of identifying and analyzing segregation and weakness in the asphalt mixture is proposed. The results show that our composite images are more accurate than traditionally-synthesized panoramic images. Also, the trained U-NET++ is ideal for recognizing and segmenting the aggregate image. The data obtained from the image can thus be used to quickly derive index parameters for the asphalt mixture. Compared with traditional test methods, this method only requires the use of mobile phones and personal computers. It is thus a lightweight and intelligent analysis method for acquiring information about asphalt–aggregate mixtures.

Research on The Application of Big Data in E-Commerce Enterprise Supply Chain Cost Control

Big data technology obtains value information by collecting relevant data of e-commerce enterprises, strengthens the cost control of the enterprise supply chain, and adopts intelligent and digital analysis methods to enhance the core competitiveness of e-commerce enterprises. Based on the application of big data technology in e-commerce supply chain cost control, the paper analyses the current problems of e-commerce enterprise supply chain cost control, and proposes a solution to the problem by applying big data technology to reduce procurement costs.

Linguistic Analysis of Internet Media and Social Network Data in the Problems of Social Transformation Assessment.

A combined approach has been developed to estimate the effectiveness of social transformations as a measure of inconsistency between the actions of the authorities and the expectations of society and the synergy (social activity) of people, based on formalized coordination of the results obtained by the method of expert assessments and methods of sentiment analysis and intelligent analysis of text messages from online open sources and social networks. These methods are implemented as a set of web services and applications in the development environment of the Advanced Analytics integrated online platform of the World Data Center “Geoinformatics and Sustainable Development.” The effectiveness of the proposed approach is demonstrated by a quantitative assessment of the attitude of the population of Ukraine to the actions of the authorities concerned with the spread of the COVID-19 pandemic.

Kaplan-Meier plotter data analysis model in early prognosis of pancreatic cancer

Intelligent data analysis methods provide helpful tools for cancer researchers to detect the prognosis of patients with specific diseases. Yet, very little information is known about the features of these models used in data analysis methods. In this study, we presented a new Kaplan-Meier plotter model with a better-combination of input features for early prognosis tasks of pancreatic cancer. Our new model integrates gender, race, and follow up the threshold to get better verification of genes of interest as prognostic markers for predicting cancer at early stages. Assessment is made for the developed model to examine the important role of the oncogene RablA in early prediction of pancreatic cancer on the standard clinical datasets from The Human Protein Atlas. Our results showed that overexpression of the oncogene Rab1A in pancreatic cancer plays a vital role in its early prognosis (p<0.05). The proposed model results were also verified using an independent dataset deposited in The Human Protein Atlas. Altogether, the experimental results highlight Rab1A potential role in cancer prognosis.

Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities

Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities’ energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries, NTLs represent around 15% of total energy generated in 2018, varying between 5 and 30% depending on the country because of the strong correlation with social, economic, political, and technical variables. According to this, electric utilities have a strong interest in finding new techniques and methods to mitigate this problem as much as possible. This research presents the results of determining with the precision of the existing data-oriented methods for detecting NTL through a methodology based on data analytics, machine learning, and artificial intelligence (multivariate data, analysis methods, classification, grouping algorithms, i.e., k-means and neural networks). The proposed methodology was implemented using the MATLAB computational tool, demonstrating improvements in the probability to identify the suspected customer’s measurement systems with error in their records that should be revised to reduce the NTLs in the distribution system and using the information from utilities’ databases associated with customer information (customer information system), the distribution grid (geographic information system), and socio-economic data. The proposed methodology was tested and validated in a real situation as a part of a recent Ecuadorian electric project.

Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications

Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of interconnected devices, allowing the use of various smart applications. The enormous number of IoT devices generates a large volume of data that requires further intelligent data analysis and processing methods such as deep learning (DL). Notably, DL algorithms, when applied to the Industrial IoT (IIoT), can provide various new applications, such as smart assembling, smart manufacturing, efficient networking, and accident detection and prevention. Motivated by these numerous applications, in this article, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, autoencoders, and recurrent neural networks, as well as their use in different industries. We then outline a variety of DL use cases for IIoT systems, including smart manufacturing, smart metering, and smart agriculture. We delineate several research challenges with the effective design and appropriate implementation of DL-IIoT. Finally, we present several future research directions to inspire and motivate further research in this area.

Rapid Blockage Diagnosis And Early Warning of Urban Drainage Pipe Network

The core of comprehensive treatment effect of water environment in river basin lies in pipe network system. The blockage of urban drainage pipe network is one of the main reasons affecting the healthy operation of urban drainage network system. So the intelligent analysis method of urban drainage pipe network blockage diagnosis can quickly find the problems of urban drainage pipe network and improve the operation and maintenance efficiency of urban drainage pipe network. In this paper, through the analysis of rainfall data, rainfall and sewage pipe network flow variation regular pattern, the change range, the essay obtains the rain and sewage pipe network blockage diagnosis threshold. Combined with the urban intelligent drainage pipe network system, through the customized setting of pipe network blockage threshold, the rapid diagnosis and early warning of the blockage problem of the drainage pipe network are realized, and the healthy operation of the urban drainage network is guaranteed.

Brain Storm Optimization Based Clustering for Learning Behavior Analysis

Recently, online learning platforms have proven to help people gain knowledge more conveniently. Since the outbreak of COVID-19 in 2020, online learning has become a mainstream mode, as many schools have adopted its format. The platforms are able to capture substantial data relating to the students’ learning activities, which could be analyzed to determine relationships between learning behaviors and study habits. As such, an intelligent analysis method is needed to process efficiently this high volume of information. Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data. This study proposes a clustering algorithm based on brain storm optimization (CBSO) to categorize students according to their learning behaviors and determine their characteristics. This enables teaching to be tailored to taken into account those results, thereby, improving the education quality over time. Specifically, we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence. The experiments are performed on the 104 students’ online learning data, and the results show that CBSO is feasible and efficient.

Modeling the volatility of financial processes

Tasks of risk management meet in all branches of practical activity of the person. Especially topical problems of mathematical modeling, estimation and forecasting of risks (which are characterized by the level of possible losses and their probability) is for the banking sector, insurance, investment companies. manufacturing enterprises operating in conditions of fierce competition and changing conditions, and for other types of activity. For the mathematical description of possible losses today there is a set of ideologically different approaches, which are based on classical statistical methods and methods of intelligent data analysis. Thus, to evaluate market and some other types of risks, various variants of the Value-at-Risk (VaR) technique are used, which makes it possible to obtain acceptable quality results for practical use.In the assessment of credit risks found the use of nonlinear models of classification type on the basis of logistical regression, linear regression, method of reference vectors (MOV), discriminatory analysis, fuzzy logic, neuro-fuzzy models, methods of Bayesian data analysis and decision tree, as well as combinations of these methods. To assess financial risks in insurance, the above-mentioned methods are used, as well as the theory of distributions of random variables, generalized linear models, regression analysis (linear and nonlinear models), Bayesian networks and other models and methods. The created computer system makes it possible to estimate the value of possible losses of VaR according to the methods of historical modeling and Monte Carlo. To solve this problem, you must first obtain estimates of the volatility forecasts of the relevant financial heteroscedatic processes. To calculate estimates of volatility forecasts, a model of generalized auto-aggression with conditional heteroscedasticity (UARUG or GARCH) is often used.

Construction and Simulation of Financial Audit Model Based on Convolutional Neural Network.

Big data has brought a new round of information revolution. Faced with the goal of full coverage of audit and supervision, making full use of big data is the main method to promote the realization of the goal of full coverage of audit and supervision. Data analysis and utilization is an indispensable task of auditing. Actively exploring multidimensional and intelligent data analysis methods and developing big data audit cases are the new development direction of auditing. The convolutional neural network's excellent ability to extract data features well meets the relevant requirements of financial auditing. However, in practical applications, convolutional neural networks often encounter various problems such as disappearance of gradients and difficulty in convergence, which reduces its expected performance in financial audit applications. In order to make the performance of the financial audit model based on convolutional neural network more excellent, after summarizing the characteristics of genetic algorithm, this article applies genetic algorithm to the optimization of the convolutional neural network model. We applied genetic algorithm to optimize the initial weights of the convolutional neural network. The error sensitivity and learning rate changes of different hidden layers are discussed, the influence of different learning rates on the convergence speed of convolutional neural networks is analyzed, and the recognition performance of other algorithms on financial audit data sets is simulated and compared. We conducted experiments on the network structure and parameter optimization on the financial audit database. The results show that the recognition error rate of the convolutional neural network model with improved learning rate algorithm in the financial audit data set is lower than that of the multilayer feedforward network, so it has better performance.

Analysis of Volleyball Video Intelligent Description Technology Based on Computer Memory Network and Attention Mechanism.

There are some problems in the process of video intelligent description and analysis of volleyball, such as poor effective information extraction rate and poor dynamic tracking effect. Based on this, combined with long-term and short-term memory network and attention mechanism, this paper designs an intelligent description model of volleyball video based on deep learning algorithm and studies how to improve the extraction rate of volleyball video information through intelligent detection hardware and image recognition technology. This paper first introduces the application of image recognition technology and deep learning algorithm in the intelligent description of volleyball video, then designs the volleyball video and image recognition model based on deep learning algorithm according to the requirements of volleyball video intelligent description, and selects three correlation factors related to the impact indicators of volleyball skills. This study selects three characteristic parameters associated with volleyball video analysis indexes, namely, take-off, bounce, and hand movement, combined with image sensing hardware assisted sensor network to realize real-time monitoring of action state in volleyball video analysis system. The experimental results show that, compared with the current mainstream sports video intelligent analysis and image recognition methods with data analysis as the core, the intelligent volleyball sports video intelligent description and image recognition system based on the integration of deep learning algorithm and sensor hardware assistance has the advantages of good detection effect, high data effectiveness, low cost, and high efficiency of volleyball sports video analysis. It can effectively improve the efficiency of volleyball video intelligent description.