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In this paper, we propose a novel paper currency defect detection algorithm using quaternion uniform strength. We first build paper currency image preprocessing integration framework which includes intensity balancing, paper currency location, and geometric correction. We then propose a global–local paper currency image registration algorithm by moving key areas within certain range which can eliminate the false difference effectively. Finally, the quaternion uniform strength is calculated by using quaternion convolution edge detector. The defect degree of paper currency is determined by using the quaternion uniform color difference. The proposed algorithm is tested using different datasets from five countries: CNY, USD, EUR, VND, and RUB. The experimental results demonstrate that the proposed algorithm yields better results than the existing state-of-the-art paper currency defect detection techniques. The demo of the proposed paper currency defect detection algorithm will be publicly available.

This Special Issue presents extended versions of the best papers presented at the International Conference on Digital Image Computing: Techniques and Applications (DICTA) which is the flagship Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established in 1991 as the premier conference of the Australian Pattern Recognition Society (APRS). However, DICTA accepts submissions from researchers all over the world and attracts a significant number of international delegates each year. DICTA is technically cosponsored by the Institute of Electrical and Electronics Engineers (IEEE) and the Australian Pattern Recognition Society (APRS). DICTA 2019 was held in Perth, Western Australia, from 2 to 4 December 2019. All papers accepted and presented at DICTA 2019 are included in the IEEE Xplore digital library. Each paper submitted to DICTA 2019 was reviewed by three experts in a double-blind review process. Based on comments from the three reviewers and assessments of the Program Chairs of the conference as well as the Guest Editors, authors of 17 papers were invited to submit extended versions of their work to this Special Issue of Neural Computing and Applications (NCAA). Each submitted paper went through a peer review process again, and 10 papers were finally accepted for publication in NCAA.

Most printed material is produced by printing halftone dot patterns. One of the key issues that determine the attainable print quality is the structure of the paper surface, but the relation is non-deterministic in nature. We examine the halftone print quality and study the statistical dependence between the defects in printed dots and the topography measurement of the unprinted paper. The work concerns SC paper samples printed by an IGT gravure test printer. We have small-scale 2D measurements of the unprinted paper surface topography and the reflectance of the print result. The measurements before and after printing are aligned with subpixel resolution, and individual printed dots are detected. First, the quality of the printed dots is studied using Self Organizing Map and clustering and the properties of the corresponding areas in the unprinted topography are examined. The printed dots are divided into high and low print quality. Features from the unprinted paper surface topography are then used to classify the corresponding paper areas using Support Vector Machine classification. The results show that the topography of the paper can explain some of the print defects. However, there are many other factors that affect the print quality, and the topography alone is not adequate to predict the print quality.

Various applications of control charts in the field of health-care monitoring and surveillance can be found in the literature. As one of the major categories, monitoring binary outcomes of cardiac surgeries with the aim of logistic regression model for the patients' death probability has been extended by different researchers. For this aim, statistical control charts, such as cumulative sum (CUSUM) chart, are applied as a risk-adjusted method to monitoring patients' mortality rate. However, employing machine learning techniques such as artificial neural network (ANN) has not been paid attention. So, this paper proposes a novel ANN-based control chart with a heuristic training approach to monitor binary surgical outcomes by control charts. Performance of the proposed approach is investigated and compared with existing studies, based on the average run lengths (ARL) criterion and the results demonstrated a superior performance of the proposed approach. Nevertheless, to demonstrate the application of the proposed approach, some real-life applications are also provided in this paper. Furthermore, robustness of the proposed method is investigated by considering Beta distribution for the death rate in addition to the logistic model. 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. Scopus

Acute lymphocytic leukemia (ALL) is a common serious cancer in white blood cells (WBC) that advances quickly and produces abnormal cells in the bone marrow. Cancerous cells associated with ALL lead to impairment of body systems. Microscopic examination of ALL in a blood sample is applied manually by hematologists with many defects. Computer-aided leukemia image detection is used to avoid human visual recognition and to provide a more accurate diagnosis. This paper employs the ensemble strategy to detect ALL cells versus normal WBCs using three stages automatically. Firstly, image pre-processing is applied to handle the unbalanced database through the oversampling process. Secondly, deep spatial features are generated using a convolution neural network (CNN). At the same time, the gated recurrent unit (GRU)-bidirectional long short-term memory (BiLSTM) architecture is utilized to extract long-distance dependent information features or temporal features to obtain active feature learning. Thirdly, a softmax function and the multiclass support vector machine (MSVM) classifier are used for the classification mission. The proposed strategy has the resilience to classify the C-NMC 2019 database into two categories by using splitting the entire dataset into 90% as training and 10% as testing datasets. The main motivation of this paper is the novelty of the proposed framework for the purposeful and accurate diagnosis of ALL images. The proposed CNN-GRU-BiLSTM-MSVM is simply stacked by existing tools. However, the empirical results on C-NMC 2019 database show that the proposed framework is useful to the ALL image recognition problem compared to previous works. The DenseNet-201 model yielded an F1-score of 96.23% and an accuracy of 96.29% using the MSVM classifier in the test dataset. The findings exhibited that the proposed strategy can be employed as a complementary diagnostic tool for ALL cells. Further, this proposed strategy will encourage researchers to augment the rare database, such as blood microscopic images by creating powerful applications in terms of combining machine learning with deep learning algorithms.

Dental X-ray image segmentation is helpful for assisting clinicians to examine tooth conditions and identify dental diseases. Fast and lightweight segmentation algorithms without using cloud computing may be required to be implemented in X-ray imaging systems. This paper aims to investigate lightweight deep learning methods for dental X-ray image segmentation for the purpose of deployment on edge devices, such as dental X-ray imaging systems. A novel lightweight neural network scheme using knowledge distillation is proposed in this paper. The proposed lightweight method and a number of existing lightweight deep learning methods were trained on a panoramic dental X-ray image data set. These lightweight methods were evaluated and compared by using several accuracy metrics. The proposed lightweight method only requires 0.33 million parameters ($$\sim 7.5$$ ∼ 7.5 megabytes) for the trained model, while it achieved the best performance in terms of IoU (0.804) and Dice (0.89) comparing to other lightweight methods. This work shows that the proposed method for dental X-ray image segmentation requires small memory storage, while it achieved comparative performance. The method could be deployed on edge devices and could potentially assist clinicians to alleviate their daily workflow and improve the quality of their analysis.

© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Differential evolutionary (DE) algorithm is one of the most frequently used evolutionary computation method for the solution of non-differentiable, complex and discontinuous real value numerical problems. The analytical structure of the mutation and crossover operators used by DE and the initial values of the parameters of the relevant operators affect the problem-solving ability of DE. Unfortunately, there is no analytical method for selecting and initializing the best artificial genetic operators that DE can use to solve a problem. Therefore, there is a need to develop new evolutionary search methods that are parameter-free and insensitive to the artificial genetic operators they use. In this paper, the Bernstein–Levy differential evolution (BDE) algorithm, which has a unique elitist-mutation operator and a Bernstein polynomials-based stochastic parameter-free crossover operator, is introduced. The numerical problem-solving success of BDE is statistically evaluated by using 30 benchmark problems of CEC2014 in the numerical experiments presented. BDE's success in solving the related benchmark problems is statistically compared with six state-of-the-art comparison algorithms. In this paper, three real-world optimization problems are also solved by using the proposed algorithm, BDE. According to statistics generated from the experimental results, BDE is statistically better than comparison methods in solving the related real-world problems.

Assessing real-time performance of Artificial Lift Pumps is a prevalent time-series problem to tackle for natural gas operators in Eastern Australia. Multiple physics, data-driven, and hybrid approaches have been investigated to analyse or predict pump performance. However, these methods present a challenge in running compute-heavy algorithms on streaming time-series data. As there is limited research on novel approaches to tackle multivariate time-series analytics for Artificial Lift systems, this paper introduces a human-in-the-loop approach, where petroleum engineers label clustered time-series data to aid in streaming analytics. We rely on our recently developed novel approach of converting streaming time-series data into heatmap images to assist with real-time pump performance analytics. During this study, we were able to automate the labelling of streaming time-series data, which helped petroleum and well surveillance engineers better manage Artificial Lift Pumps through machine learning supported exception-based surveillance. The streaming analytics system developed as part of this research used historical time-series data from three hundred and fifty-nine (359) coal seam gas wells. The developed method is currently used by two natural gas operators, where the operators can accurately detect ten (10) performance-related events and five (5) anomalous events. This paper serves a two-fold purpose; first, we describe a step-by-step methodology that readers can use to reproduce the clustering method for multivariate time-series data. Second, we demonstrate how a human-in-the-loop approach adds value to the proposed method and achieves real-world results.

ExSeisDat is designed using standard message passing interface (MPI) library for seismic data processing on high-performance super-computing clusters. These clusters are generally designed for efficient execution of complex tasks including large size IO. The IO performance degradation issues arise when multiple processes try accessing data from parallel networked storage. These complications are caused by restrictive protocols running by a parallel file system (PFS) controlling the disks and due to less advancement in storage hardware itself as well. This requires and leads to the tuning of specific configuration parameters to optimize the IO performance, commonly not considered by users focused on writing parallel application. Despite its consideration, the changes in configuration parameters are required from case to case. It adds up to further degradation in IO performance for a large SEG-Y format seismic data file scaling to petabytes. The SEG-Y IO and file sorting operations are the two of the main features of ExSeisDat. This research paper proposes technique to optimize these SEG-Y operations based on artificial neural networks (ANNs). The optimization involves auto-tuning of the related configuration parameters, using IO bandwidth prediction by the trained ANN models through machine learning (ML) process. Furthermore, we discuss the impact on prediction accuracy and statistical analysis of auto-tuning bandwidth results, by the variation in hidden layers nodes configuration of the ANNs. The results have shown the overall improvement in bandwidth performance up to 108.8% and 237.4% in the combined SEG-Y IO and file sorting operations test cases, respectively. Therefore, this paper has demonstrated the significant gain in SEG-Y seismic data bandwidth performance by auto-tuning the parameters settings on runtime by using an ML approach.

Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

The spreading of machine learning (ML) and deep learning (DL) methods in different and critical application domains, like medicine and healthcare, introduces many opportunities but raises risks and opens ethical issues, mainly attaining to the lack of transparency. This contribution deals with the lack of transparency of ML and DL models focusing on the lack of trust in predictions and decisions generated. In this sense, this paper establishes a measure, namely Congruity, to provide information about the reliability of ML/DL model results. Congruity is defined by the lattice extracted through the formal concept analysis built on the training data. It measures how much the incoming data items are close to the ones used at the training stage of the ML and DL models. The general idea is that the reliability of trained model results is highly correlated with the similarity of input data and the training set. The objective of the paper is to demonstrate the correlation between the Congruity and the well-known Accuracy of the whole ML/DL model. Experimental results reveal that the value of correlation between Congruity and Accuracy of ML model is greater than 80% by varying ML models.

The spread of Covid-19 misinformation on social media had significant real-world consequences, and it raised fears among internet users since the pandemic has begun. Researchers from all over the world have shown an interest in developing deception classification methods to reduce the issue. Despite numerous obstacles that can thwart the efforts, the researchers aim to create an automated, stable, accurate, and effective mechanism for misinformation classification. In this paper, a systematic literature review is conducted to analyse the state-of-the-art related to the classification of misinformation on social media. IEEE Xplore, SpringerLink, ScienceDirect, Scopus, TaylorFrancis, Wiley, Google Scholar are used as databases to find relevant papers since 2018-2021. Firstly, the study begins by reviewing the history of the issues surrounding Covid-19 misinformation and its effects on social media users. Secondly, various neuro-fuzzy and neural network classification methods are identified. Thirdly, the strength, limitations, and challenges of neuro-fuzzy and neural network approaches are verified for the classification misinformation specially in case of Covid-19. Finally, the most efficient hybrid method of neuro-fuzzy and neural networks in terms of performance accuracy is discovered. This study is wrapped up by suggesting a hybrid ANFIS-DNN model for improving Covid-19 misinformation classification. The results of this study can be served as a roadmap for future research on misinformation classification.

The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching-learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature.

The abundance of digital documents offers a valuable chance to gain insights into public opinion, social structure, and dynamics. However, the scale and volume of these digital collections makes manual analysis approaches extremely costly and not scalable. In this paper, we study the potential of using automated methods from natural language processing and machine learning, in particular weak supervision strategies, to understand how news influence decision making in society. Besides proposing a weak supervision solution for the task, which replaces manual labeling to a certain extent, we propose an improvement of a recently published economic index. This index is known as economic policy uncertainty (EPU) index and has been shown to correlate to indicators such as firm investment, employment, and excess market returns. In summary, in this paper, we present an automated data efficient approach based on weak supervision and deep learning (BERT + WS) for identification of news articles about economical uncertainty and adapt the calculation of EPU to the proposed strategy. Experimental results reveal that our approach (BERT + WS) improves over the baseline method centered in keyword search, which is currently used to construct the EPU index. The improvement is over 20 points in precision, reducing the false positive rate typical to the use of keywords.

With the development of informatization, traditional industries have been seriously impacted by cross-border e-commerce (CBEC), and the financial management mode no longer meets the needs of CBEC users. As cross-border electronic trading platforms face enormous financial data challenges, they need financial supervision and effective network risk protection. Artificial intelligence (AI) can effectively improve the financial accounting ability and resource integration of platforms. However, at present, the underdeveloped financial management system of CBEC platforms and problems, such as the running records of platform funds have seriously hindered the development of the financial standardization of platforms because the financial management system affects managers’ statistical analysis of financial data. Therefore, this paper analyzes the financial risks, the management problems and the causes of these problems of CBEC platforms and then uses AI to study the financial operation of CBEC platforms. Subsequently, this paper uses a machine learning (ML) algorithm to analyze the financial data clustering center and the security factor of the financial model. Finally, this paper proposes some corresponding strategies for financial model optimization and construction that can improve the information security and capital management of CBEC finance and promote the long-term development of CBEC platforms. The experimental results show that the classifier value and the safety factor of the financial model of CBEC platforms gradually increase under the ML algorithm. The mean value of the classifier value is approximately 1.14, and the mean value of the safety factor is approximately 1.37. Overall, the initial value of the classifier value of the platform financial model is 0.85, which increases to 1.41 on the seventh day, while the whole process increases by 0.56. The initial value of the safety factor of the platform financial model is 1.10, which increases to 1.64 on the seventh day, while the whole process increases by 0.54. The transaction information security and financial accounting accuracy of the CBEC platform financial model is better than that of the original financial model. The transaction information security is 10.4% higher than that of the original financial model, and the financial accounting accuracy is 10% higher than that of the original financial model. In other words, both AI and ML can promote the financial accounting standards and long-term development of CBEC platforms.

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people’s sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people’s needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs.

Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques.

Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models.

Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

Modeling of mean monthly flow is of particular importance for long-term planning of processes relying on water abstraction, such as reservoir operations. Advantage of data-driven models for these applications is the ability to predict monthly streamflow based on the combined hydrological and climatological input data. Methodology and recommendations for implementation of supervised learning (SL), from choice of input variables and optimization of model parameters in dependence of dataset size to final model evaluation, remains generally undefined. The main objective of this paper is to model mean monthly flow by SL models, while optimization algorithms (genetic algorithm-GA and simulated annealing-SA) are used to optimize and automate the choice of parameters and the reliable set of input variables. Detailed analysis of accuracy and amount of time needed to build three supervised learning models (ANN, SVM and NNM) for modeling of mean monthly flow is given in the paper. The 40- years input dataset has been shown as long enough for building models of satisfying quality, and was used in the further analysis where GA and SA were used first for optimization of model parameters, and later, for simultaneous optimization of both model parameters and input variables for SL models. In the analysis, time series was always split in building, calibration and verification part, while optimization was done on the building and calibration part. Data outside the particular time series was used for additional verification. Optimization of the model parameters by the exhaustive search indicated that the most accurate models were ANN and SVM, overperforming NNM across all data subsets, revealing that models need to be built with external variables. Optimization using GA and SA with SVM produced obvious movement toward optimal values, especially when the choice of parameters and input variables was optimized with GA-SVM.

In the present work, total specific pore volume of inorganic polymers (geopolymers) made from seeded fly ash and rice husk–bark ash has been predicted by gene expression programming. To build the model, training and testing using experimental results from 120 specimens were conducted. The values for input layers were the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk–bark ash in the ashes mixture, the percentage of coarse rice husk–bark ash in the ashes mixture, the temperature of curing, and the time of water curing. According to the input parameters, in the gene expression programming models, the pore volume of each specimen was predicted. The training and testing results in the gene expression programming models have shown a strong potential for predicting the total specific pore volume of the geopolymer specimens.

To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns as biomarkers of brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed handwriting representation and estimation methods presented in the literature and discusses their strengths and weaknesses. It also highlights the limitations of existing processes and the challenges commonly faced when designing such systems. High-level directions for further research conclude the paper.

Hadoop, a distributed computing framework that can efficiently process large-scale datasets, has been used by an increasing number of organizations as the basic computing framework to build cloud computing platforms. Improving its execution efficiency is a hot research direction in the industry, and the scheduling problem is a key factor affecting the execution efficiency of Hadoop. It is very important to identify its shortcomings and improve them. This paper examines and analyses the optimization of computing task scheduling performance based on the Hadoop big data platform. This paper first analyses Hadoop big data processing. Hadoop has high scalability. Computing nodes can be added at any time, and they can participate in cluster work through simple configuration. The paper discusses the improvement in the Hadoop resource scheduling algorithm. The task scheduling algorithm in the Hadoop-based data task localization proposed in this paper is compared with the default algorithm used in the Hadoop task scheduling algorithm. The former shows better local data in all four jobs, there are more data localization tasks, and the expected goal is achieved. The effectiveness of the algorithm is verified, and the performance is improved by 30%.

Portfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebalancing of portfolios with vigorous risk through an RL agent. This is done while accounting for market conditions, asset diversifications, risk and returns in the global financial market. Studies have been performed in this paper to explore four types of methods with variations in fully portfolio rebalancing and gradual portfolio rebalancing, which combine with and without the use of the Long Short-Term Memory (LSTM) model to predict stock prices for adjusting the technical indicator centring. Performances of the four methods have been evaluated and compared using three constructed financial portfolios, including one portfolio with global market index assets with different risk levels, and two portfolios with uncorrelated stock assets from different sectors and risk levels. Observed from the experiment results, the proposed RL agent for gradual portfolio rebalancing with the LSTM model on price prediction outperforms the other three methods, as well as returns of individual assets in these three portfolios. The improvements of the returns using the RL agent for gradual rebalancing with prediction model are achieved at about 27.9–93.4% over those of the full rebalancing without prediction model. It has demonstrated the ability to dynamically adjust portfolio compositions according to the market trends, risks and returns of the global indices and stock assets.

In order to improve the target recognition effect of small UAV (unmanned aerial vehicle) remote sensing image, this paper proposes a new super-resolution reconstruction method based on the recurrent convolutional network, which can achieve different degrees of super-resolution effect by controlling the number of cycles. Moreover, it can control the number of iterations of small UAVs with different degrees of blur and can be better adapted to the recognition scenarios of UAVs. In addition, this paper studies the target recognition method of small UAV remote sensing image, combines fuzzy clustering method to construct the intelligent remote sensing image target recognition model, combines it with the UAV structure, realizes remote sensing recognition by UAV, and designs experiments to analyze the effect of remote sensing recognition. Further, this paper improves the recognition algorithm and positioning algorithm of remote sensing image, so that recognition and positioning of UAV video remote sensing image can get better results. Finally, this paper verifies the performance of the system through simulation experiments. The research results show that the method proposed in this paper has certain reliability.

In order to improve the effect of e-government work, it is necessary to analyze its influencing factors. E-government is affected by many social factors, so it is necessary to combine intelligent models to improve the effect of factor analysis. This paper combines the essence of e-government influencing factor data to improve the machine learning algorithm and uses the EM algorithm to derive the parameter estimation formula of the data in the case of missing data to improve the accuracy of data analysis. Moreover, this article combines the structure of the e-government system to build the main structure of the intelligent analysis model of the influence factors of e-government. According to the key influencing factor model of e-government adoption and multi-dimensional research on technology, organization and implementation, this paper puts forward the model and promotion mode of the e-government system based on cloud computing and conducts a simulation. From the simulation results, the effect of the model proposed in this paper is more significant.

In this paper, the finite-time stability of a class of fractional-order bidirectional associative memory neural networks(FBAMNNs) with time delay is concerned. Based on the monotonicity of function, a new inequality is proved. For $$0< \alpha < 1$$ and $$1< \alpha < 2$$ , based on the properties of the fractional derivative, the method of step and the fractional Gronwall inequality or the generalized Gronwall inequality, some new criteria on the finite-time stability of FBAMNNs are derived. Finally, three numerical examples are provided to show the effectiveness and superiority of the criteria obtained in this paper.

We demonstrate several techniques to encourage practical uses of neural networks for fluid flow estimation. In the present paper, three perspectives which are remaining challenges for applications of machine learning to fluid dynamics are considered: 1. interpretability of machine-learned results, 2. bulking out of training data, and 3. generalizability of neural networks. For the interpretability, we first demonstrate two methods to observe the internal procedure of neural networks, i.e., visualization of hidden layers and application of gradient-weighted class activation mapping (Grad-CAM), applied to canonical fluid flow estimation problems -- $(1)$ drag coefficient estimation of a cylinder wake and $(2)$ velocity estimation from particle images. It is exemplified that both approaches can successfully tell us evidences of the great capability of machine learning-based estimations. We then utilize some techniques to bulk out training data for super-resolution analysis and temporal prediction for cylinder wake and NOAA sea surface temperature data to demonstrate that sufficient training of neural networks with limited amount of training data can be achieved for fluid flow problems. The generalizability of machine learning model is also discussed by accounting for the perspectives of inter/extrapolation of training data, considering super-resolution of wakes behind two parallel cylinders. We find that various flow patterns generated by complex interaction between two cylinders can be reconstructed well, even for the test configurations regarding the distance factor. The present paper can be a significant step toward practical uses of neural networks for both laminar and turbulent flow problems., 24 pages, 24 figures

The heterogeneity of enterprise management will have a certain impact on enterprise investment. In order to explore the strength of this correlation, this paper builds an intelligent system that can be used for enterprise analysis with the support of machine learning technology, uses embedded algorithms to improve the traditional algorithm structure, and combines machine learning to optimize and analyze data processing. Moreover, this paper combines the heterogeneity of enterprise management and the actual needs of enterprise management, the current financial market investment status to analyze the system's functional structure, and combine the actual needs to construct the overall system architecture. In addition, this paper adopts algorithm analysis to the system process, analyzes the system logic layer structure, and builds the overall system structure framework. Finally, this paper designs an experiment to analyze the performance of the system constructed in this paper. From the experimental research, it can be seen that the system constructed in this paper basically meets the expected requirements.

Recently, wide installations of photovoltaic (PV) systems have been achieved in the electrical power systems. However, fluctuated output power of the PV generation and/or fluctuated load demands represent critical factors for the operation of PV systems. Thence, energy storage systems (ESSs) are highly needed for improving the supply reliability of PV generation systems. Among existing ESSs, the proton exchange membrane fuel cell (PEMFC) systems represent long-lifetime, efficient, and cost-effective solutions for PV systems. However, nonlinear behaviour exists in the output of PEMFC systems, which depends on the operating cell temperature, and the membrane water contents. The output of PEMFC has a unique operating maximum power point (MPP) for each operating combination of membrane water content and temperature, which requires MPP tracking (MPPT) control loop. Therefore, this paper presents a fuzzy logic control (FLC) MPPT method for enhancing the operation of PEMFC systems. The traditional MPPT methods in the literature employ three sensors, including voltage, water content, and temperature. The proposed controller employs only the PEMFC output current and voltage electrical signals. Compared to the classical fixed step size perturb and observe (P and O) and hill climbing MPPT methods, the proposed method represents variable step size MPPT method. In addition, compared to the widely employed incremental conductance (INC) and incremental resistance (INR) MPPT methods, the proposed method benefits the wide operating and adaptive step size MPPT operation due to using the FLC approach. The proposed method is simple and can be implemented using low cost microcontrollers. The design procedures, operating principle, and performance verification of the proposed method are presented in this paper.

Over the past years, a digital multimedia uprising has been experienced in every walk of life, due to which the un-annotated or unstructured multimedia content has always been a key issue for research. The multimedia content is usually created with some intended emotions, which the creator wants to induce in viewers. The affectiveness of the multimedia content can be measured by analyzing elicited emotions of its viewers. In this paper, we present a rigorous study of human cognition using EEG signals while watching a video, to analyze the affectiveness of video content. The analysis presented in this paper is done to establish an effective relationship between video content and the human emotional state. For this, the most effective scalp location and frequency ranges are identified for two categories of videos, i.e., excited and sad. Furthermore, a common affective response (CAR) is extracted for finding the distinguishable features for aforementioned categories of videos. The CAR is calculated and tested on the publicly available dataset “AMIGOS,” and the results presented here show the utility of cognitive features on extracted scalp locations and frequency ranges for automatic tagging of video content. The current research explores the innovative applicability of neuro-signals for a mouse-free video tagging based on human excitement level to augment a range of brain–computer interface (BCI)-based devices. It can further aid to automatically retrieve the video content which is exciting and interesting to human viewers. With this analysis, we aimed to provide a thorough analysis which can be used to customize a low-cost and mobile EEG system for automatic analysis and retrieval of videos.

Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extracted components are nonnegative and usually sparse. Nevertheless, the sparsity is only a side effect and cannot be explicitly controlled without additional regularization. In this paper, we investigated the nonnegative CANDECOMP/PARAFAC (NCP) decomposition with the sparse regularization item using $$l_1$$ l 1 -norm (sparse NCP). When high sparsity is imposed, the factor matrices will contain more zero components and will not be of full column rank. Thus, the sparse NCP is prone to rank deficiency, and the algorithms of sparse NCP may not converge. In this paper, we proposed a novel model of sparse NCP with the proximal algorithm. The subproblems in the new model are strongly convex in the block coordinate descent (BCD) framework. Therefore, the new sparse NCP provides a full column rank condition and guarantees to converge to a stationary point. In addition, we proposed an inexact BCD scheme for sparse NCP, where each subproblem is updated multiple times to speed up the computation. In order to prove the effectiveness and efficiency of the sparse NCP with the proximal algorithm, we employed two optimization algorithms to solve the model, including inexact alternating nonnegative quadratic programming and inexact hierarchical alternating least squares. We evaluated the proposed sparse NCP methods by experiments on synthetic, real-world, small-scale, and large-scale tensor data. The experimental results demonstrate that our proposed algorithms can efficiently impose sparsity on factor matrices, extract meaningful sparse components, and outperform state-of-the-art methods.

This paper analyzes the inherent relationship between convolutional neural networks and sparse representation and proposes an improved convolutional neural network model for image synthesis in response to problems with current methods. In the testing phase, the calculation of the sparse coefficients involves the solution of complex optimization problems, which greatly reduce the operating efficiency, inspired by the successful application of convolutional neural networks in the field of image reconstruction. Compared with the traditional image portrait synthesis method, this model not only has an end-to-end closed form but also does not need to solve complex optimization problems in the synthesis stage. The synthesis experiment on an image dataset shows that this method not only improves the synthesis effect but also improves the efficiency of the traditional method by one to two orders of magnitude, demonstrating its potential application value. Blocking processing is a common method for sparse domain image modeling. It improves the computational efficiency but also decreases the global structure of the image, which is difficult to compensate for through the aggregation and overlap of image blocks. In response to this problem, this paper proposes a low-rank image inpainting method based on a Gaussian mixture model. This method embeds the local statistical characteristics of image blocks into the kernel norm model and not only uses the Gaussian mixture model to maintain the local details of the image but also describes the global low-rank structure of the image through the kernel norm, thus restoring a class of image data with a potential low-rank structure and theoretically revealing the structured sparse nature of the Gaussian mixture model. This paper optimizes the strategy based on random hidden neuron nodes and proposes a dropout anti-overfitting strategy based on sparsity. The experiments show that this strategy can effectively improve the convergence speed while ensuring good performance and can effectively prevent overfitting.

The development of green finance and the quantitative evaluation of its impact on the ecological environment provide empirical evidence for the construction of the carbon trading accounting system. Among them, carbon trading is an important part of green finance, and the accounting of businesses related to carbon emission rights has promoted the development of regional green finance. In order to explore the relationship between green finance and carbon emissions, this paper builds an analysis model of the relationship between green finance and carbon emissions based on big data and machine learning based on big data technology and machine learning technology. Moreover, this paper conducts simulation tests through the system and compares the output results with the actual situation after system simulation to verify the effectiveness of the model in this paper. From the experimental research results, it can be seen that the correlation analysis model of green finance and carbon emissions based on big data and machine learning constructed in this paper has a good performance in the correlation analysis of green finance and carbon emissions. Moreover, it is not difficult to see through the model of this paper that there is a clear correlation between green finance and carbon emissions.

To improve the effect of landslide feature extraction, this paper improves the remote sensing image recognition algorithm with the support of a machine learning algorithm. Moreover, this paper combines UAV remote sensing images to extract landslide features, classifies and introduces the evaluation criteria for target detection and several representative target detectors. This paper also constructs the functional structure of the system according to the landslide feature extraction requirements and designs a set of optimization schemes for landslide feature data collection and control measurement suitable for field operations. In addition, this paper analyses the system kernel algorithm process and analyses the system function realization through simulation research. Finally, this paper designs an experiment to evaluate the practicability of the system constructed in this paper. From the results of experimental statistics, we can see that the system constructed in this paper has good practicability.

The path planning system is an important part of unmanned vehicles, and the development of path planning technology will surely promote the rapid development of unmanned vehicle technology. In order to prevent the node from continuously monitoring its state, a self-triggering control strategy is proposed. Before the trigger moment, the node does not need to monitor its state. Moreover, considering the unpredictable problem of the node state, a control strategy triggered by the observed event is proposed, that is, only the output state information is used to determine the trigger time. In addition, this paper analyzes and models the two major factors that affect the local planning results, the environment and the vehicle, and uses the path smoothing and optimization method based on B-spline curve and the path optimization method based on the steering controller. Finally, this paper designs experiments to analyze the vehicle local path planning method and time consistency of the unmanned driving system. From the experimental results, it can be seen that the unmanned driving system constructed in this paper has a certain effect.

An offline handwriting recognition system for Urdu, a language with a user base of 200 Million and written in Nastaleeq script, has been a challenge for the research community. The key problems include recognition of complex ligature shapes and lack of publicly available datasets. This paper addresses both these problems by (i) proposing an end-to-end handwriting recognition system based on a new CNN-RNN architecture with n-gram language modeling, and (ii) presenting a new unconstrained dataset called NUST-UHWR. We compiled the first unconstrained Urdu handwritten data from around 1000 people from diverse background, age and gender population. The text in this dataset is selected carefully from seven different fields to ensure the presence of commonly used words in different domains. The model architecture is capable of incorporating fine-grained features necessary for handwritten text recognition of complex ligature languages. Our method addresses the limitations of existing architectures and provides state-of-the-art performance on Urdu handwritten text. We achieve a minimum character error rate of 5.28% on Urdu handwriting recognition (UHWR) and establish a state-of-the-art. The paper further demonstrates the generalization ability of the proposed model by training on English language and bilingual (Urdu and English) handwritten data.

Traditional navigation systems rely mainly on satellite navigation, but internal positioning and navigation cannot be achieved. This is mainly due to the inability to penetrate the wall due to the complex internal environment and the signal reaching the ground. Therefore, this paper proposes the research of indoor positioning algorithm based on improved convolutional neural network. The main method of this paper is based on the monocular vision indoor positioning based on the improved convolutional neural network. This method is an intelligent solution to the problems of traditional methods such as high cost, poor anti-interference ability, weak robustness, and poor compatibility. Positioning aids. The traditional computer vision camera pose estimation method is affected by the complex background in the image. When extracting feature corners, it is very affected by non-interest corners, so this paper adds an improved convolutional neural network algorithm to add in complex indoor scenes. With the region limitation, camera pose estimation in the region of interest is better to achieve low-cost, high accuracy and more stability. The improved model in this paper was tested on a test set of indoor datasets with markers, and the recognition accuracy reached 98.1%. In addition, this paper improves the PnP solution method, compares and analyzes the traditional RPnP, EPnP, and CEPPnP algorithms, and seeks the optimal camera pose estimation algorithm to locate the world coordinates of the current shooting position. The experimental results show that this paper proposes the positioning algorithm has high stability.

Buried pipeline is threatened from the soil displacement due to earthquake and other causes, which leads to the formation of unexpected external forces such as bending moment. The problem can be worse with the appearance of defects, resulting in the reduction in pipe capacity. The paper focuses on the overall problem of defected pipe crossing the strike-slip fault. A full-scaled FE model can be very complicated with the large-scale and micro-scale levels corresponding to the strike-slip fault and defect on pipe problems, respectively. To ease this difficulty, the macro and micro problems are solved separately with two types of FE models and their corresponding databases. To be specific, one FE model is used for predicting external moment due to strike-slip fault and the other is for predicting the moment capacity of defected pipe. Data-driven models are consequently developed with artificial neural network (ANN) for each database generated from these types of models: ANN1 evaluating moment capacity of defected pipe (R2 is 0.9943 on test set) and ANN2 predicting both moment and axial force appeared in pipe due to strike-slip fault (R-squares are 0.9883 and 0.9929 on test set, respectively). Consequently, the stress–strength analysis for the overall problem is solved. Accounting for the unavoidable uncertainty of the models, the paper proposed an approach which assumes that the actual distribution of residual of a model is equivalent to this of the test set. The distributions of residuals on test set of these ANNs are tested to be normally distributed and generated by the conventional Monte Carlo simulation. To the end, the deterministic problem leads to the failure probability. The proposed framework has been investigated, and the final results on this selective parametric study are reasonable.

People’s cognition of objectively existing things is developing in the direction of digitization, and more and more objectively existing things are constantly being presented in the form of data. In order to realize the analysis of constantly changing large-scale data and the automatic extraction of valuable information, this paper analyzes the application of artificial intelligence auxiliary equipment based on machine learning in the cognitive learning process, and proposes a collaborative filtering method based on fusion of global and local parameters. Moreover, this paper studies a cognitive computing model based on context-aware data stream to realize effective analysis of context-aware data and obtain effective cognitive results. In addition, this paper proposes a task scheduling algorithm based on improved queue matching to solve the task scheduling problem in a distributed computing environment. Finally, this paper constructs an improved algorithmic cognitive system architecture, and verifies the performance of the system through experimental research. The research results show that the system constructed in this paper is highly reliable.

The traditional dance training process lacks a certain degree of scientificity due to the lack of precise motion capture and analysis system, which directly affects the final training effect. In view of the robust limitations of the type 1 fuzzy reinforcement learning flexible structure control system to the uncertainty of noise interference, based on the reinforcement learning algorithm, this paper proposes a flexible structure controller based on the type 2 fuzzy reinforcement learning algorithm. Moreover, this paper uses fuzzy sets with equidistant fuzzy centers to divide large-scale state or continuous state space into two types of fuzzy divisions, divide the action space uniformly, and build fuzzy rules based on the basic ideas of type 1 fuzzy reinforcement learning. In addition, this paper constructs an evaluation system for factors affecting dance training effects based on reinforcement learning and designs experiments to verify the performance of the system. The research results show that the system constructed in this paper meets the theoretical needs and can be applied to dance training practice later.

In cloud services and Internet-of-Things (IoT) applications, the cybersecurity in video transmission technologies has drawn much attention in recent researches due to the rapid growth of cyber-risks on both individuals and institutions. Unfortunately, the spoofing attack, a kind of cyber-risk, has increased the number of cyber-criminals in data transfer applications without being detected, especially in smart cities. Several applications based on online video communications, such as online testing and video conferences, are involved in smart cities. The video displays various variations of a person, which makes face recognition an important concept in security implementation. The face spoofing attacks are mainly based on the person's face replication by replaying a video or by printed photos. Therefore, video forgery detection and related spoof attack detection have become a new topic in cybersecurity research. From this perspective, this paper presents a deep learning approach for video face forensic detection with a cyber facial spoofing attack using two methodologies. The first methodology is based on a convolutional neural network (CNN) to extract features from the input video frames. The model has five convolutional layers followed by five pooling layers. The second methodology is based on convolutional long short-term memory (ConvLSTM). This model comprises two pooling layers, two convolutional layers, and a convolutional LSTM layer. Each methodology includes a fully-connected layer to interconnect between the feature map resulting from the feature extraction process and the classification layer. A SoftMax layer performs the classification task in each method. This paper aims to achieve an optimum modality for face forensic detection to overcome spoofing attacks. Simulation results reveal that the ConvLSTM with CNN methodology achieves better classification results as the extracted features are more comprehensive than those of other conventional approaches. Also, it achieves an accuracy of 99%, and the works presented in the literature achieve an accuracy levels up to 95%.

In order to improve the effectiveness of signal processing, according to the actual needs of signal processing and the current problems in signal processing, this paper introduces an improved LSTM recurrent neural network algorithm to construct the signal processing algorithm. Moreover, this paper sets up the functional structure of this paper based on the neural network model structure, and builds an intelligent analysis system for signal processing tasks based on the LSTM recurrent neural network algorithm. In addition, this paper analyzes the system algorithm flow in detail, and combines experimental research to carry out the effectiveness of the system constructed in this paper, and conducts quantitative analysis from two aspects of signal threshold prediction and signal processing effect. Finally, this paper conducts experimental results research with the support of mathematical statistics methods. From the research point of view, it can be known that the system constructed in this paper has good signal processing functions.

With the ever-improving degree of car privatization, the number of car-owning residents has repeatedly reached new highs, but it has also caused traffic congestion, especially lane congestion. To reduce emergency lane congestion, and realize the intelligent detection and classification of emergency lanes, this paper introduces logistic regression theory and proposes a vehicle detection method based on logistic regression. Based on the powerful feature abstraction and automatic learning capabilities of convolutional networks, it studies vehicles based on convolutional networks. The recognition algorithm analyses the contribution of the convolution kernel in the convolutional network, and transforms the original emergency lane vehicle detection. The research results show that the emergency lane vehicle detection rate based on logistic regression and a deep convolutional network designed in this paper can reach approximately 98%, which is higher than the detection methods based on SVM and AdaBoost, and the detection time is not different from SVM and AdaBoost. After training, the convolution kernel of the deep convolutional network, the detection time and detection accuracy improved to a certain extent. This shows that the emergency lane vehicle detection and classification method based on logistic regression and a deep convolutional network, can play an important role in emergency lane intelligence.

The massive number of research articles on the Web makes it troublesome for researchers to identify related works that could meet their preferences and interests. Consequently, various network representation learning-based models have been proposed to produce citation recommendations. Nevertheless, these models do not exploit semantic relations and contextual information between the objects of bibliographic papers’ networks, which can result in inadequate citation recommendations. Moreover, existing citation recommendation methods face problems such as lack of personalization, cold-start, and network sparsity. To mitigate such problems and produce individualized citation recommendations, we propose a heterogeneous network embedding model that jointly learns node representations by exploiting semantics corresponding to the author, time, context, field of study, citations, and topics. Compared to baseline models, the results produced by the proposed model over the DBLP datasets prove 10% and 12% improvement on mean average precision (MAP) and normalized discounted cumulative gain (nDCG@10) metrics, respectively. Also, the effectiveness of our model is analyzed on the cold-start papers and network sparsity problems, where it gains 12% and 9% better MAP and recall@10 scores, respectively.

Owing to the exponential intensification of the web information content and transfiguration of the conventional Web into a more sophisticated Semantic Web, there is a mandated need for semantics aware web recommendation systems. In this paper, a graph-based semantic strategy for query recommendation has been proposed. Firstly, the paper aims at the construction of the proposed Query Sense Concept Tripartite graph for the initially built Query Click Graph. Secondly, a novel river flow dynamics strategy has been proposed for the selection of the best concept entailment path for integrating real-world knowledge from semantic wikis. Thirdly, an adaptation of the Tversky Index with parametric variations for computing the semantic similarity further contributes to novelty. Finally, a horizontal–vertical grid-based dynamic capture of user intents has been proposed for understanding the current informational needs of the user. Experimentations have been conducted for both the AOL and the SIGIR datasets for computing the performance of the Query Recommendation. An accuracy of 0.86 and 0.91 has been achieved for the AOL and the SIGIR datasets with a very low FDR of 0.11 and 0.07, respectively, which is the best-in-class performance when benchmarked with the baseline methods.

Coronary heart disease is a heart disease caused by coronary atherosclerosis, which seriously endangers human life and health. More and more studies have shown that diabetes is one of the main pathogenic factors of coronary heart disease and has an important relationship with coronary heart disease. At present, the mainstream treatment of coronary heart disease complicated with diabetes mellitus is the use of coronary angiography, which is the gold standard of treatment at present. However, it still has a certain risk, and most of the postoperative complications, the improvement method is to use FFR evaluation standard. At present, there are few special researches on this aspect. Therefore, this paper proposes the application research of FFR based on deep learning in the diagnosis of coronary heart disease complicated with diabetes mellitus. Through the core theoretical research on coronary heart disease complicated with diabetes mellitus and FFR, this paper analyzes that the existing coronary angiography is still in the development stage, which has a positive effect on the treatment of coronary heart disease and diabetes mellitus. On this basis, combined with FFR can play a better therapeutic effect. The second part is the establishment method of the related comparative experiment. This experiment adopts real coronary heart disease complicated with diabetes cases, through the way of random grouping, each group of 41 people, one group was FFRCT group, the other was FFRQCA group, and in order to ensure the experimental effect, a unified evaluation index is established. In the third part, the comparative analysis of the experimental methods of angina pectoris was carried out. Through the analysis of experimental data, it is shown that the safety, postoperative complications control and comprehensive treatment effect of this method are significantly improved compared with the traditional methods.

In order to solve the problem of crop disease detection in large-scale planting, a new crop disease detection algorithm based on multi-feature decision fusion is proposed. This paper proposes a multi-feature decision fusion disease discrimination algorithm (PD R-CNN) based on machine vision on crop surfaces. The algorithm is based on the machine vision processing model of R-CNN and integrates a disease discrimination algorithm on the basis of R-CNN. After training on crop image data sets, PD R-CNN can reach the goal of identifying crop surface lesions. This paper uses machine vision image acquisition, image processing and analysis technology to collect and analyze the growth of cucumber seedlings. The research results show that compared with manual judgment, PD R-CNN reduces the workload and can effectively distinguish crop diseases. Through experiments, during the occurrence of pests and diseases, PD R-CNN has a monitoring accuracy of 88.0% for mosaic disease, 92.0% for root rot, 88.0% for powdery mildew, and 86.0% for aphids, indicating that there are errors in actual monitoring, but the accuracy exceeds 85.0% can be put into use.

This paper addresses a tracking control problem with prespecified accuracy for uncertain switching nonlinear systems under arbitrary switching with periodically time-varying parameters. To approximate the unknown nonlinear functions and unknown periodically time-varying parameters, the radial basis function neural network and Fourier series expansion are introduced, respectively. Compared with the previous results of unknown nonlinear switching system approximation, the upper bounds of the approximation errors are considered for the first time. And then, a new adaptive NN control scheme is constructed by using backstepping technique and common Lyapunov function theory. It can be proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to the prescribed neighborhood of zero. Two examples are provided to verify the feasibility and advantages of the proposed approach in this paper.

To improve the scientificity and reliability of medical behavior analysis, this paper combines machine learning and wireless sensor technology to construct an intelligent data mining system that can be used for medical behavior analysis and uses association rules to analyze and mine the implicit relationships between structural monitoring parameters. Moreover, this paper establishes strong association rules between different monitoring variables based on historical monitoring data under normal structural conditions to predict whether the structural conditions are normal. In addition, this paper constructs a system function module according to actual needs, obtains the overall system architecture, and implements the system function module in combination with algorithms. Finally, this paper designs experiments to verify the performance of the system constructed in this paper and discusses the experimental results through mathematical graph analysis methods. From the research point of view, it can be observed that the system constructed in this paper has a specific effect.