BULDP: Biomimetic Uncorrelated Locality Discriminant Projection for Feature Extraction in Face Recognition

Analysis of Sentiment and Personalised Recommendation in Musical Performance

Music performance research is a comprehensive study of aspects such as emotional analysis and personalisation in music performance, which help to add richness and creativity to the art of music performance. The labels in this paper in collaborative annotation contain rich personalised descriptive information as well as item content information and can therefore be used to help provide better recommendations. The algorithm is based on bipartite graph node structure similarity and restarted random wandering. It analyses the connection between users, items, and tags in the music social network, firstly constructs the adjacency relationship between music and tags, obtains the music recommendation list and indirectly associated music collection, then fuses the results according to the proposed algorithm, and reorders them to obtain the final recommendation list, thus realising the personalised music recommendation algorithm. The experiments show that the proposed method can meet the personalised demand of users for music on this dataset.

Aided Recognition and Training of Music Features Based on the Internet of Things and Artificial Intelligence

With the development of the Internet of Things, many industries have been on the train of the information age, and digital audio technology is also constantly developing. Music retrieval has gradually become a research hotspot in the music industry. Among them, the auxiliary recognition of music characteristics is also a particularly important Task. Music retrieval is mainly to manually extract music signals, but now the music signal extraction technology has encountered a bottleneck. The article uses Internet and artificial intelligence technology to design an SNN music feature recognition model to identify and classify music features. The research results of the article show (1) statistic graphs of the main melody and accompanying melody of different music. The absolute value of the main melody and accompanying melody mainly fluctuates in the range of 0–7, and the proportion of the main melody can reach 36%. The accompanying melody can reach 17%. After the absolute value of the interval reaches 13, the interval ratio of the main melody and the accompanying melody tends to be stable, maintaining between 0.6 and 0.9, and the melody interval ratio value completely coincides; the main melody in the interval variable is X. (1) The relative difference value in the interval of −X(16) fluctuates greatly. After the absolute value of the interval reaches 17, the interval ratio of the main melody and the accompanying melody tends to be stable, maintaining between 0.01 and 0.04 and the main melody. The value of the difference is always higher than the accompanying melody. (2) When the number of feature maps is 24∗5, the recognition result is the most accurate, MAP recognition result can reach 78.8, and the recognition result of precision@ is 79.2; when the feature map size is 5∗5, the recognition result is the most accurate, MAP recognition result can reach 78.9, the recognition result of precision@ is 79.2, and the recognition result of HAM2 (%) is 78.6. The detection accuracy of the SNN music recognition model proposed in the article is the highest. When the number of bits is 64, the detection accuracy of the SNN detection model is 59.2%, and the detection accuracy of the improved SNN music recognition model is 79.3%, which is better than the detection rate of ITQ music recognition model of 17.9%, which is 61.4% higher. The experimental data further shows that the detection efficiency of the ITQ music recognition model is the highest. (3) The SNN music recognition model proposed in the article has the highest detection accuracy, regardless of whether it is in a noisy or no-noise music environment, with an accuracy rate of 97.97% and a detection accuracy value of 0.88, which is 5 types of music. The highest one among the recognition models, the ITQ music recognition model, has the lowest detection accuracy, with a detection accuracy of 67.47% in the absence of noise and a detection accuracy of 70.23% in the presence of noise. Although there is a certain noise removal technology, it can suppress noise interference to a certain extent, but cannot accurately describe music information, and the detection accuracy rate is also low.

Martial Arts Routine Training Method Based on Artificial Intelligence and Big Data of Lactate Measurement

As a traditional Chinese sport, competitive martial arts routines have a long history. The competition rules are the unified norms and standards formulated for sports competitions. They are a yardstick for referees to judge the technical level and competitive ability of athletes and an essential basis for coaches during training. In particular, the new rules increase the difficulty of martial arts routines training and score, improve the balance movement of various groups, highlight the action specifications, increase the proportion of the score, and strengthen the scoring measures for the performance level. Subsequently, this puts higher requirements for the exceptional technical level of routine athletes. Therefore, it is vital to formulate scientific martial arts systematic training methods. This paper considers the above problem and current popular artificial intelligence technology and constructs a neural network algorithm to solve it. In addition, since lactic acid is a good monitoring indicator of the training load intensity and effect of martial arts routine exercises, this article also considers extensive lactate measurement data to construct martial arts systematic training methods. Through simulations, our experimental verification and the obtained results demonstrate the effectiveness of the proposed algorithm.

Intelligent Prediction and Optimization Algorithm for Chronic Disease Rehabilitation in Sports Using Big Data

This paper investigates chronic diseases in the older population in the Chinese province of Henan and analyzes the rehabilitation needs and the current supply of related services in different levels of medical and elderly care institutions. We explore the fundamental causes for the diversified needs and insufficient supply of chronic disease patients in professional medical services and daily care. Using big data and deep learning (DL) in the sports domain, we propose a novel and intelligent prediction system for chronic diseases. Our model explores effective sinking methods of high-quality medical resources, training and guidance practices, assistance and guidance measures, and the ability to improve the grassroots services so that more chronically ill populations can stay in the community family as long as possible. In such an environment, they can receive cheap, safe, and suitable services. It can also lead to further improvement in constructing the government's regional medical rehabilitation care service system and can formulate long-term care relevant compensation policies.

Multi-angle head pose classification with masks based on color texture analysis and stack generalization

Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H-channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.

Falling-Point Recognition and Scoring Algorithm in Table Tennis Using Dual-Channel Target Motion Detection

In table tennis, the ball has numerous characteristics of high speed, small size, and changeable trajectory. Due to these characteristics, the human eye often cannot accurately judge the ball's movement and position, leading to the problem of precise detection of the ball's falling point and movement tracking. In sports, the use of machine learning for locating and detecting the ball and the use of deep learning for reconstructing and displaying the ball's trajectories are considered futuristic technologies. Therefore, this paper proposes a novel algorithm for identifying and scoring points in table tennis based on dual-channel target motion detection. The proposed algorithm consists of multiple input channels to jointly learn different features of table tennis images. The original image is used as the input of the first channel, and then the Sobel operator is used to extract the first-order derivative feature of the original image, which is used as the input of the second channel. The table tennis feature information from the two channels is then fused and sent to the 3D neural network module. The fully connected layer is used to identify the table tennis ball's drop point, compare it with a standard drop point, calculate the error distance, and give a score. We also constructed a data set and conducted experiments. The experimental results show that the method in this paper is effective in sports.

Application of Data Mining in the Analysis of Martial Arts Athlete Competition Skills and Tactics

In martial arts, data mining technologies are used to describe and analyze the moves of athletes and changes in the process and sequences. Martial arts is a process in which athletes use all kinds of strengths and actions to make offensive and defensive changes according to the tactics of opponents. One such martial arts is Wushu arts as it has a long history in reference to Chinese martial arts. During the Wushu competition, Wushu athletes show their adaptability and technical level in complex, random, and nonlinear competitive abilities, organized and systematic skills, tactics, and position movement. Using data mining techniques, in-depth mining a particular type of martial arts competition technology and tactics behind statistical data, and using the data to find the law of change to solve some problems, for martial arts athletes in daily training to develop technology and tactics and improve competition results, is the practical significance of data mining in martial arts athletes competition. This research explored the relationship between goal-oriented and mental intensity and their effect on competitive success outcomes.

Footballer Action Tracking and Intervention Using Deep Learning Algorithm

Fédération Internationale de Football Association is the governing body of the football world cup. The international tournament of football requires extensive training of all football players and athletes. In the training process of footballers, players and coaches recognize the training actions completed by footballers. The training actions are compared with standard actions, calculate losses, and scientifically intervene in the training processes. This intervention is important for better results during the training sessions. Coaches must determine and confirm that every action performed by the footballers meets the minimum standards. It is because the actions of individual players are performed quickly; as a result, the coach's eye may not produce accurate results as human activities are prone to errors. Therefore, this paper designs and develops a footballer's motion and gesture recognition and intervention algorithm using a convolutional neural network (CNN). In this proposed algorithm, initially, texture features and HSV features of the footballer's posture image are extracted and then a dual-channel CNN is constructed. Each characteristic is extracted separately, and the output of the dual-channel network is combined. Finally, the obtained results are passed from a fully connected CNN to estimate and construct the posture image of the footballer. This article performs experimental testing and comparative analysis on a wide range of data and also conducts ablation studies. The experimental work shows that the proposed algorithm achieves better performance results.

A Novel Multi-task Tensor Correlation Neural Network for Facial Attribute Prediction

Multi-task learning plays an important role in face multi-attribute prediction. At present, most researches excavate the shared information between attributes by sharing all convolutional layers. However, it is not appropriate to treat the low-level and high-level features of the face multi-attribute equally, because the high-level features are more biased toward the specific content of the category. In this article, a novel multi-attribute tensor correlation neural network (MTCN) is used to predict face attributes. MTCN shares all attribute features at the low-level layers, and then distinguishes each attribute feature at the high-level layers. To better excavate the correlations among high-level attribute features, each sub-network explores useful information from other networks to enhance its original information. Then a tensor canonical correlation analysis method is used to seek the correlations among the highest-level attributes, which enhances the original information of each attribute. After that, these features are mapped into a highly correlated space through the correlation matrix. Finally, we use sufficient experiments to verify the performance of MTCN on the CelebA and LFWA datasets and our MTCN achieves the best performance compared with the latest multi-attribute recognition algorithms under the same settings.

Research on multi-effect evaporation salt prediction based on feature extraction

In the multi-effect evaporation salt making process, the smooth operation of the salt making process is crucial. As the salt production process continues, many unstable factors will cause the salt production process not to proceed smoothly. These factors can be discovered in advance by predicting the salt production data, thus, it is of great significance to predict the multi-effect evaporation salt production data. In the process of multi-effect evaporation and salt production, the multiple salt-making devices make the influence between the parameters closer, and the influence of a single parameter on itself is sometimes ductile. Therefore, the data of multi-effect evaporation and salt production have the characteristics of high dimensions, high complexity and temporal information. If the historical salt production data is used for data prediction directly, the prediction model will take a long time and the prediction effect is not good. Thus, how to predict the multi-effect evaporation salt production data is the main research problem of this paper. In view of the above problems, according to the characteristics of multi-effect evaporation salt production data, this paper analyzes and improves the self encoder for feature extraction of multi effect-evaporation salt production data, so as to solve the problem of high dimensions and high complexity of salt production data. On this basis, combined with the time-series information contained in the salt production data, a multi-effect evaporation salt production data prediction model is proposed based on long-term and short-term memory cycle neural network to solve the prediction problem of time-series salt production data. Experiments show that the prediction model can predict and prevent the problems in salt production line in advance. It has a certain theoretical research value and application value in the intelligent production process and production line optimization of salt chemical industry.

Robust Face Recognition Based on a New Supervised Kernel Subspace Learning Method

Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named “supervised kernel locality-based discriminant neighborhood embedding,” which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only nonlinear and complex variation of face images is effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, in order to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. Experiment results reveal that our method consistently outperforms its competitors, which demonstrates strong potential to be implemented in many real-world systems.

Integrating Gaze Tracking and Head-Motion Prediction for Mobile Device Authentication: A Proof of Concept

We introduce a two-stream model to use reflexive eye movements for smart mobile device authentication. Our model is based on two pre-trained neural networks, iTracker and PredNet, targeting two independent tasks: (i) gaze tracking and (ii) future frame prediction. We design a procedure to randomly generate the visual stimulus on the screen of mobile device, and the frontal camera will simultaneously capture head motions of the user as one watches it. Then, iTracker calculates the gaze-coordinates error which is treated as a static feature. To solve the imprecise gaze-coordinates caused by the low resolution of the frontal camera, we further take advantage of PredNet to extract the dynamic features between consecutive frames. In order to resist traditional attacks (shoulder surfing and impersonation attacks) during the procedure of mobile device authentication, we innovatively combine static features and dynamic features to train a 2-class support vector machine (SVM) classifier. The experiment results show that the classifier achieves accuracy of 98.6% to authenticate the user identity of mobile devices.