Hybrid fuzzy based spearman rank correlation for cranial nerve palsy detection in MIoT environment

Topic prediction for tobacco control based on COP9 tweets using machine learning techniques

The prediction of tweets associated with specific topics offers the potential to automatically focus on and understand online discussions surrounding these issues. This paper introduces a comprehensive approach that centers on the topic of "harm reduction" within the broader context of tobacco control. The study leveraged tweets from the period surrounding the ninth Conference of the Parties to review the Framework Convention on Tobacco Control (COP9) as a case study to pilot this approach. By using Latent Dirichlet Allocation (LDA)-based topic modeling, the study successfully categorized tweets related to harm reduction. Subsequently, various machine learning techniques were employed to predict these topics, achieving a prediction accuracy of 91.87% using the Random Forest algorithm. Additionally, the study explored correlations between retweets and sentiment scores. It also conducted a toxicity analysis to understand the extent to which online conversations lacked neutrality. Understanding the topics, sentiment, and toxicity of Twitter data is crucial for identifying public opinion and its formation. By specifically focusing on the topic of "harm reduction" in tweets related to COP9, the findings offer valuable insights into online discussions surrounding tobacco control. This understanding can aid policymakers in effectively informing the public and garnering public support, ultimately contributing to the successful implementation of tobacco control policies.

Machine learning model-based two-dimensional matrix computation model for human motion and dance recovery

Many regions of human movement capturing are commonly used. Still, it includes a complicated capturing method, and the obtained information contains missing information invariably due to the human's body or clothing structure. Recovery of motion that aims to recover from degraded observation and the underlying complete sequence of motion is still a difficult task, because the nonlinear structure and the filming property is integrated into the movements. Machine learning model based two-dimensional matrix computation (MM-TDMC) approach demonstrates promising performance in short-term motion recovery problems. However, the theoretical guarantee for the recovery of nonlinear movement information lacks in the two-dimensional matrix computation model developed for linear information. To overcome this drawback, this study proposes MM-TDMC for human motion and dance recovery. The advantages of the machine learning-based Two-dimensional matrix computation model for human motion and dance recovery shows extensive experimental results and comparisons with auto-conditioned recurrent neural network, multimodal corpus, low-rank matrix completion, and kinect sensors methods.

Smart healthcare solutions using the internet of medical things for hand gesture recognition system

Patient gesture recognition is a promising method to gain knowledge and assist patients. Healthcare monitoring systems integrated with the Internet of Things (IoT) paradigm to perform the remote solutions for the acquiring inputs. In recent years, wearable sensors, and information and communication technologies are assisting for remote monitoring and recommendations in smart healthcare. In this paper, the dependable gesture recognition (DGR) using a series learning method for identifying the action of patient monitoring through remote access is presented. The gesture recognition systems connect to the end-user (remote) and the patient for instantaneous gesture identification. The gesture is recognized by the analysis of the intermediate and structuring features using series learning. The proposed gesture recognition system is capable of monitoring patient activities and differentiating the gestures from the regular actions to improve the convergence. Gesture recognition through remote monitoring is indistinguishable due to the preliminary errors. Further, it is convertible using series learning. Therefore, the misdetections and classifications are promptly identified using the DGR and verified by comparative analysis and experimental study. From the analysis, the proposed DGR approach attains 94.92% high precision for the varying gestures and 89.85% high accuracy for varying mess factor. The proposed DGR reduces recognition time to 4.97 s and 4.93 s for the varying gestures and mess factor, respectively.

Hybrid fuzzy interface model of sports rehabilitation activities

At present, the relevant test data and training indicators of athletes during rehabilitation training lack screening and analysis, so it is impossible to establish a long-term longitudinal tracking research system and evaluation system. In order to improve the practical effect of sports rehabilitation activities, this paper successively introduces the matrix normal mixed model and the fuzzy clustering algorithm based on the K-L information entropy regularization and the matrix normal mixed model. Moreover, this paper uses the expectation maximization algorithm to estimate the parameters of the model, discusses the framework, key technologies and core services of the development platform, and conducts certain research on the related technologies of the three-tier architecture. At the same time, according to the actual needs of sports rehabilitation training, this paper designs the functions required for exercise detection and prescription formulation. In addition, this paper analyzes and designs the database structure involved in each subsystem. Finally, this paper designs experiments to verify the performance of the model constructed in this paper. The research results show that the performance of the model constructed in this paper meets the expectations of model construction, so it can be applied to practice.

Unprofessional problems and potential healthcare risks in individuals' social media use

BACKGROUND

In recent years, social media have filtered our life both in the professional and personal aspects. Currently, most of us suffer from poor quality of thinking, which is due to the impact of social media towards our lives, particularly in the health care arena.

OBJECTIVES

In this article, cultural tension due to social media creates an unwanted risk to the youngsters and others with sleep deprivation. They become dependent on staying dynamic via social networking sites media all the time. As indicated by an ongoing report, there is a reliable connection between the measure of time spent via web-based networking media and depression among youthful grown-ups, which creates unprofessional problems and potential healthcare risk in individuals due to the usage of social media.

RESULTS

This article speaks about the research gap and possible risks reforming strategies on healthcare communication in social media through statistical analysis.

CONCLUSION

The experimental validation of case studies shows prominent solutions that have not been addressed in traditional methods.

Performance evaluation of enterprises’ innovation capacity based on fuzzy system model and convolutional neural network

Influenced by national policies and macro-economic environment, large domestic enterprises is actively promoting strategic transformation to enhance their core competitiveness, and performance evaluation of enterprises’ innovation capacity has become a hot topic in recent years. This paper proposes a performance evaluation method of enterprises’ innovation capacity based on deep learning fuzzy system model and convolutional neural network analysis of innovation network. First of all, on account of the characteristics of breakthrough innovation and drawing on the traditional innovation performance evaluation model, this paper constructs a breakthrough innovation performance evaluation index system for enterprises from the six dimensions of main resource input, technology out-turn, process management, product performance, social value and commercial Value. Secondly, the introduction of machine learning of fuzzy convolutional neural network to assess the advancement execution of enterprises is of great significance for enterprise managers to find out the problems and causes of enterprises’ innovation, optimize the allocation of enterprises’ resources and further improve the innovation performance of enterprises. The experimental results show to verify the adequacy of the algorithm.

Public opinion analysis of complex network information of local similarity clustering based on intelligent fuzzy system

With the rise of the network society, as the mapping Internet space, the public opinion has become the most active way of expressing social public opinion. It gradually gets deeply involved in the development and change of various social phenomena, social problems and social events, and evolves into the real politics and public management. In this context, it is of great practical significance to explore the evolution process and laws of online public opinions and systematically analyze the influence mechanism in the evolution process of online public opinions. This paper comprehensively uses the modeling simulation, empirical analysis, fuzzy systems and other research methods, adopts the reasonable abstraction of the main behavior characteristics, behavior motives and network relations of network users, and then constructs the evolution model of network public opinion in the complex social network. Besides, from the new research perspective of network members and network relations of the dynamic interaction between the government, media and netizen, this paper makes an in-depth study on the influence mechanism of the dynamic evolution of online public opinion.

A risk investment evaluation method based on dynamic bayesian network and fuzzy system

In order to enhance the risk investment evaluation algorithm precision of forestry rights mortgage of farmers, this paper provides a method of risk investment validating process of forestry rights mortgage of farmers based on dynamic Bayes network (DBN) and fuzzy system. For that have to be processed fuzzy data in time arrangement and evaluate the circumstance viably, Intuitionistic Fuzzy Dynamic Bayesian Network (IFDBN) is assembled. Intuitionistic fuzzy thinking is implanted into DBN as a virtual node in this method. Also, another technique to change over the intuitionistic fuzzy thinking yield into likelihood that could contribution to DBN as proof is proposed. Firstly, it analyzes the risk investment of forestry rights mortgage of farmers, raises the risk evaluation system and adopts normalization and factor analysis methods to pre-process the model index; secondly, by aid of a four-layer DBN model, it puts forward the hierarchical DBN model of risk investment, having input layer, fuzzy layer, fuzzy inference layer and output layer, designs the composition and calculation mode of fuzzy function module and DBN module; Finally, it verifies the viability of the calculation through experimental examination.

A pretreatment method of wastewater based on artificial intelligence and fuzzy neural network system

A pretreatment method of industrial saline wastewater based on Artificial Intelligence based fuzzy neural network analysis was proposed to improve the pretreatment accuracy of industrial saline wastewater. This method uses a four-layer AI fuzzy neural network model and proposes a graded fuzzy neural network model for pretreatment method of industrial saline wastewater, it includes input layer, fuzzification layer, fuzzy logical layer and output layer, and designs the framework and calculation mode of the fuzzy function block and the neural network module. Finally, the dynamic simulation experiments of dissolved oxygen control in the fifth zone and nitrate nitrogen control in the second zone are carried out based on the simulation benchmark model (BSM1) platform. The experimental results show that this approach can effectively raise the adaptive control accuracy of the system compared with PID, feed forward neural network and conventional recurrent neural network.

Analysis of vibration high-frequency dynamic characteristics of wheelless rail vehicle system based on fuzzy logic control

Track irregularity is the main source of vibration excitation of vehicle-track weighing system, which has an important impact on the safety, stability and comfort of train operation, and it is also the main factor limiting the speed of train operation. Higher requirements are put forward for track smoothness with the rapid development of China’s railways. Therefore, it has a great theoretical and practical significance to study the relationship between track irregularity and random vibration of vehicle-track cooker-in system and the evaluation method of track smoothness. In this paper, a model construction method is proposed based on machine learning fuzzy logic control and neural network algorithm to analyze the high frequency dynamic characteristics of ballastless track wheel-rail vehicle system. Firstly, a numerical analysis model of high frequency dynamic characteristics of ballastless track wheel-rail vehicle system is established by using linear discrete elastic-yellow damper element connection model; Secondly, the Rough Set Block Neural Network is introduced to optimize the dynamic characteristic analysis model, and the intelligent model of model optimization analysis is established. Finally, the validity of the proposed algorithm is verified by simulation experiments of practical examples.

Forecast of export demand based on artificial neural network and fuzzy system theory

This paper analyses the significance and methods of foreign trade export forecasting. The index system of foreign trade export forecasting is determined based on the analysis of foreign trade export forecasting research results. The concepts and principles of artificial neural network and fuzzy system theory are expounded, and their respective advantages and disadvantages as well as their complementarities are analyzed. This paper introduces the types and training algorithms of evolutionary morphological neural network, combines the neural network with the fuzzy system theory, and establishes the prediction model. Finally, the evolutionary morphological neural network model is applied to the prediction of foreign trade export in view of the characteristics of export and considering the influence of various factors, the whole process of establishing evolutionary morphological neural network forecasting model is introduced in detail, and the change range of export is predicted, and the ideal forecasting results are obtained.

Wireless sensor network model with uncertain delay and packet loss based on intelligent fuzzy system

The utilization of fuzzy logic in WSNs is demonstrated to be a promising procedure since it permits joining and assessing various parameters in an effective way. Fuzzy logic is a decent methodology because of the execution prerequisites can be effectively supported by sensor hubs, while it can improve the general system execution. This paper studies the robust H∞ control considering time delay and packet loss related uncertainty in wireless sensor network system based on the basic theory of intelligent fuzzy systems. The model of a wireless sensor network with questionable time lag and packet loss is given first. The stability of the system is proved by the augmented Lyapunov functional and the linear matrix inequality (LMIs) method, with its demonstrated H∞ property. In order to solve the uncertain time delay and packet loss, the memory robust H∞ controller is proposed based on LMIs. Numerical examples and simulation results examines the potency of the presented method in solving the delay and packet loss of wireless sensor networks as well as the accuracy and precision of the system.

Permanent magnet synchronous motor algorithms based on nonlinear identification generalized predictive and Intelligent fuzzy control system

A control strategy of permanent magnet-oriented field synchronous motor based on intelligent fuzzy control system and generalized predictive control with non-linear identification is proposed to develop the effectiveness of the controlling method of constant magnet-oriented field synchronous motor, the accessor can be split into stabilization control part and intelligent control part. The input of traditional feedback control is used as the stabilization control part, while the feed-forward is incorporated into the intelligent part to compensate for the uncertainties of repetitive load torque and model parameters. The proposed feed forward compensation term uses simple learning rules without any load torque disturbance observer. The additional learning feed forward term does not require information about motor parameters and load torque values, it is insensitive to load torque uncertainty and model parameters, and does not need to identify the system model. With that, the solidness and intermingling confirmation of the proposed control framework reaction is given. The exploratory outcomes demonstrate that the proposed technique has littler speed overshoot list, and the heap torque against aggravation capacity list is improved by over 30%.