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Artificial intelligence (AI) proves decisive in today's rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F1-score are 1.00, 0.960, 0.974, and 0.980, respectively. [ABSTRACT FROM AUTHOR]

In digital media art, expressive force is an important art form of media. This paper studies digital images that have the same effect when applied to media art. The research object is media art images, and the application effect of the proposed algorithm is related to the media art images. The development of digital image technology has brought revolutionary changes to traditional media art expression techniques. In this paper, a partial-pixel interpolation technique based on convolutional neural network is proposed. Supervised training of convolutional neural networks requires predetermining the input and target output of the network, namely, integer image and fractional image in this paper. To solve the problem that the subpixel sample cannot be obtained, this paper first analyzes the imaging principle of digital image and proposes a subpixel sample generation algorithm based on Gaussian low-pass filter and polyphase sampling. From the perspective of rate distortion optimization, the purpose of pixel motion compensation is to improve the accuracy of interframe prediction. Therefore, this paper defines pixel motion compensation as an interframe regression problem, that is, the mapping process of the reference image integral pixel sample to the current image sample to be encoded. In this paper, a generalized partial-pixel interpolation model is proposed for bidirectional prediction. The partial-pixel interpolation of bidirectional prediction is regarded as a binary regression model; that is, the integral pixel reference block in two directions is mapped to the current block to be coded. It further studies how to apply the trained digital images to media art design more flexibly and efficiently. [ABSTRACT FROM AUTHOR]

We combine machine learning with Internet of Things technology to study the performance of network propagation model. This paper first introduces the construction environment of the business push system and then realizes user clustering and active business push by using the experimental data. Experimental results show that the active service push system constructed in this paper is feasible and effective. The experiment also compares and analyzes the influence of different clustering methods on the accuracy of service push. The results show that the clustering effect of the multi-Markov chain model (m-MCM) method is superior to that of the K-means method, a commonly used machine learning method, and the accuracy of user-service push obtained by the m-MCM method is superior to that obtained by the K-means method. Finally, on the basis of the existing experimental results, the shortcomings of the service push system are summarized, the future improvement direction and specific implementation measures are proposed, and new requirements for the future update of the service push system are put forward. [ABSTRACT FROM AUTHOR]

With the continuous development of digital technology, music, as an important form of media, and its digital audio technology is also constantly developing, forcing the traditional music industry to start the road of digital transformation. What kind of method can be used to automatically retrieve music information effectively and quickly in vocal singing has become one of the current research topics that has attracted much attention. Aiming at this problem, it is of great research significance for the field of timbre feature recognition. With the in-depth research on timbre feature recognition, the research on timbre feature extraction by machine learning in vocal singing has also been gradually carried out, and its performance advantages are of great significance to solve the problem of automatic retrieval of music information. This paper aims to study the application of feature extraction algorithm based on machine learning in timbre feature extraction in vocal singing. Through the analysis and research of machine learning and feature extraction methods, it can be applied to the construction of timbre feature extraction algorithms to solve the problem of automatic retrieval of music information. This paper analyzed vocal singing, machine learning, and feature extraction, experimentally analyzed the performance of the method, and used related theoretical formulas to explain. The results have showed that the method for timbre feature extraction in the vocal singing environment was more accurate than the traditional method, the difference between the two was 24.27%, and the proportion of satisfied users was increased by 33%. It can be seen that this method can meet the needs of users for timbre feature extraction in the use of music software, and the work efficiency and user satisfaction are greatly improved. [ABSTRACT FROM AUTHOR]

The present paper deals with optimizing the stock portfolio of active companies listed on the Tehran Stock Exchange based on the forecast price. This paper is based on a combination of different filtering methods such as optimization of trading rules based on technical analysis (ROC, SMA, EMA, WMA, and MACD at six levels—Very Very Weak (VVW), Very Weak (VW), Weak (W), Strong (S), Very Strong (VS), and Very Very Strong (VVS)), Markov Chains, and Machine Learning (Random Forest and Support Vector Machine) Filter stock exchanges and provide buy signals between 2011 and 2020. In proportion to each combination of filtering methods, a buy signal is issued and based on the mean-variance (M-V) model, the stock portfolio is optimized based on increasing the portfolio return and minimizing the stock portfolio risk. Based on this, out of 480 companies listed on the Tehran Stock Exchange, 85 active companies have been selected and stock portfolio optimization is based on two algorithms, MOGWO and NSGA II. The analysis results show that the use of SVM learning machine leads to minor correlation error than the random forest method. Therefore, this method was used to predict stock prices. Based on the results, it was observed that if the shares of companies are filtered, the risk of transactions decreases, and the return on the stock portfolio increases. Also, if two filtering methods are applied simultaneously, the stock portfolio returns slightly and the risk increases. In the analysis, MOGWO algorithm has obtained 133.13% stock return rate with a risk of 3.346%, while the stock portfolio returns in NSGA II algorithm 107.73, with a risk of 1.459%. Comparison of solution methods shows that the MOGWO algorithm has high efficiency in stock portfolio optimization. [ABSTRACT FROM AUTHOR]

In the field of mechanical and electrical equipment, the motor rolling bearing is a workpiece that is extremely prone to damage and failure. However, the traditional fault diagnosis methods cannot keep up with the development pace of the times because they need complex manual pretreatment or the support of specific expert experience and knowledge. As a rising star, the data-driven fault diagnosis methods are increasingly favored by scholars and experts at home and abroad. The convolutional neural network has been widely used because of its powerful feature extraction ability for all kinds of complex information and its outstanding research results in image processing, target tracking, target diagnosis, time-frequency analysis, and other scenes. Therefore, this paper introduces a convolutional neural network and applies it to motor-bearing fault diagnosis. Aiming at the shortcomings of fault signal and convolutional neural network, a large-scale maximum pooling strategy is proposed and optimized by wavelet transform to improve the fault diagnosis efficiency of motor bearing under high-voltage operation. Compared with other machine learning algorithms, the convolution neural network fault diagnosis model constructed in this paper not only has high accuracy (up to 0.9871) and low error (only 0.032) but also is simple to use. It provides a new way for motor bearing fault diagnosis and has very important economic and social value. [ABSTRACT FROM AUTHOR]

When studying intelligent community and property management system, network service has an important impact on intelligent community construction and property management service. How to use machine learning and other technologies to improve the network service of intelligent community and integrate it into real estate property management is worthy of further research. This paper introduces the basic model of machine learning and proposes a network data prediction model based on time series. For the time dimension, an improved prediction algorithm model of machine learning is proposed. For mobile data allocation, from the perspective of ensuring the current and future continuity of the spectrum after spectrum allocation, this paper proposes a spectrum allocation algorithm based on the joint measurement of time domain and frequency domain. In addition, the VP-tree algorithm is used to construct the spatial vector relationship of the intelligent community. At the same time, in the time trend and periodicity of the mobile data in the intelligent community network, the attention mechanism is introduced to realize the distribution of mobile data and traffic prediction in the intelligent community by machine learning. This paper analyzes the requirements of the property management system, designs the property management information system including the field subsystem layer, data acquisition layer, and cloud service layer, introduces the property management module and customer service module in detail, and carries out the system test. The test results show that the system runs well. Finally, aiming at the problems existing in the property management industry, this paper puts forward the development strategy of property management. [ABSTRACT FROM AUTHOR]

With the development of teaching evaluation program, colleges and universities have reformed according to the actual situation of the school. With the development of evaluation activities, many universities are eager to establish their own teaching quality evaluation system, so as to pre-evaluate the teaching quality of schools. SVM is one of the most widely used machine learning algorithms that enables efficient statistical learning with a very limited number of samples. Considering the excellent learning performance of SVM, it is very suitable for the teaching quality evaluation system. In this paper, we optimize the existing multiple classification algorithm for binary trees and propose a new method. Learning the popular teaching quality evaluation system in colleges and universities, the binary tree support vector machine classification algorithm, and design comparison experiment, the experimental results show that the evaluation model proposed in this paper has strong generalization ability and higher classification accuracy and better classification efficiency. [ABSTRACT FROM AUTHOR]

At present, due to the large-scale use of different kinds of power electronic devices in the power system, the problem of harmonic pollution in the power grid is becoming more and more serious, which will lead to a serious decline in the production, transmission, and utilization rate of electric energy, overheat electrical devices, generate vibration and interference, and then affect the aging and service life of the lines. In order to effectively reduce the harmonic problems caused by different levels of the power system, it is necessary to analyze the harmonic components. In this paper, the BP neural network learning algorithm is introduced into the harmonic problems of the power system. The mapping relationship between input and output signals is obtained by using the BP neural network algorithm, and the harmonic frequency, amplitude, and phase contained in the obtained data are analyzed. According to the type of equipment with problems in the operation of the power system and the rapid diagnosis of existing defects, the problems are quickly located and the causes are analyzed. The practical results show that the BP neural network learning algorithm proposed in this paper has higher detection accuracy and analysis speed for the difficult problems in the power system. [ABSTRACT FROM AUTHOR]

The existing deep learning models have problems such as large weight parameters and slow inference speed of equipment. In practical applications such as fire detection, they often cannot be deployed on equipment with limited resources due to the huge amount of parameters and low efficiency. In response to this problem, this paper proposes a lightweight smoke detection model based on the convolutional attention mechanism module. The model is based on the YOLOv5 lightweight framework. The backbone network draws on the GhostNet design idea, replaces the CSP structure of the FPN and head layers with the GhostBottleNeck module, adds a convolutional attention mechanism module to the backbone network layer, and uses the CIoU loss function to improve the regression accuracy. Using YOLOv5s as the benchmark model, the parameter amount of the proposed lightweight neural network model is 2.75 M, and the floating-point calculation amount is 2.56 G, which is much lower than the parameter amount and calculation amount of the benchmark model. Tested on the public fire dataset, compared with the traditional deep learning algorithm, the model proposed in the paper has better detection performance and the detection speed is significantly better than the benchmark model. Tested under the unquantized simulator, the speed of the proposed model to detect a single picture is 60 ms, which can meet the requirements of real-time engineering applications. [ABSTRACT FROM AUTHOR]

Based on a machine learning algorithm, this paper deeply explores the privacy protection of personal information. In this paper, the definition of the machine learning algorithm is put forward, the design idea of privacy protection in joint machine learning platform is studied, and the architecture model and model parameter updating strategy of joint machine learning under privacy protection are designed. To protect the privacy of personal information, this paper also proposes a data homomorphic verification mechanism to prevent the global parameters from being tampered with by malicious cloud servers. In order to verify the performance of the models constructed in this paper, the comparative experiments of different models are carried out. The experimental results show that this algorithm has a fast convergence speed, and the average error rate decreases by 4.17% compared with the traditional algorithm. Moreover, the accuracy of this algorithm reaches 95.37%, which is about 8.76% higher than the previous algorithm. This model is applied to the field of personal information privacy protection, which can provide a safe and reliable environment for personal information privacy and effectively protect the privacy of data owners. And, means and reference value is provided for the development direction of privacy protection. [ABSTRACT FROM AUTHOR]

Machine learning is an interdisciplinary study of how to make computer programs perform similar to human learning, and its techniques are widely used in the medical industry. The purpose of this paper is to study how to use machine learning-based intelligent medicine to analyze and study the assessment of athletes' physique and health status and describe the machine learning algorithm. This paper puts forward the problem of intelligent medical diagnosis, which is based on machine learning, and then elaborates on the concept of machine learning and related algorithms, and designs and analyzes a case of an athlete's physique monitoring and health status assessment system. The experimental results show that the athlete's physical fitness monitoring and health status evaluation system can meet the needs of users. The text classification effect based on the LSTM method is slightly inferior to the SVM effect, in which the recall rate of diabetes is not more than 40%, and the recall rate of cerebral infarction is improved by 26.5% after using fuzzy matching. [ABSTRACT FROM AUTHOR]

AIOps (artificial intelligence for IT operations) has been growing rapidly in recent years. However, it can be seen that the vast majority of AIOps applications are implemented in the IT domain. In contrast, there are few applications in the data center infrastructure domain. Many real-world practices show that a working architecture or algorithm cannot be directly replicated from other domains due to completely different business scenarios and different data characteristics. In this paper, an AIOps architecture for the data center infrastructure monitoring domain is presented. A proven working architecture is given in terms of core modules, such as technical architecture, machine learning algorithms, big data, and business applications, and details are designed in practice. This paper focuses on the technical part and not on each part of the architecture work. In other words, NFRs (nonfunctional requirements), such as performance, availability, manageability, and security, will not be discussed in this paper. [ABSTRACT FROM AUTHOR]

Based on ML algorithm, this paper puts forward a method that can search instructional resources through keyword indexing technology, and then cluster and recombine the related results and present them centrally. In this paper, the semantic processing of user query based on the subject index of educational resources is adopted to make up for the deficiency of query semantics, solve the problem of mismatch between query words and document words, and improve the recall and precision of resource retrieval. It is proposed to select the category feature items manually and establish the category feature model. In the environment of small sample set, the weight of category feature items is trained by ML method. The research shows that the user rating of this system is ideal, reaching 93.21% at the highest. In addition, the stability of the system can still reach 89.31% under the condition of relatively large usage, and its performance is excellent. This system can effectively solve the problem of scattered distribution of English instructional resources and make the presentation of knowledge more in line with the needs of users, thereby further improving the utilization rate of English instructional resources and users' satisfaction. [ABSTRACT FROM AUTHOR]

This paper proposes corresponding teaching methods and instructional modes based on predecessors' research on mathematics instructional mode and the current state of mathematics teaching. In addition, this paper constructs a teaching evaluation model based on DL algorithm based on an in-depth study of DL-related theories in order to accurately and scientifically analyze the problems that exist in mathematics teaching. This paper constructs an instructional quality evaluation index system based on rationality and fairness, and uses the BPNN evaluation model to train and study a set of instructional quality data. Finally, the experimental results show that this system has a high level of stability, with a 96.37 percent stability rate and a 95.42 percent evaluation accuracy rate. The results of this paper's evaluation of the mathematical instructional quality model are objective and reasonable. It can accurately assess instructional quality while also assessing problems in the teaching process based on the instructional quality scores and making reasonable recommendations for teaching improvement based on the weak links in the teaching process. It has the potential to provide a workable system for assessing instructional quality. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of emerging Internet of Things technology and short-range wireless communication technology, smart healthcare monitoring network technology has become a research hotspot. It provides convenience for people and enhances the development of people's own healthcare awareness. This paper aims to study how to make its application in the field of smart healthcare education more applicable through the use of related technologies in the Internet of Things era and few-shot learning. For this reason, this paper proposes to optimize and improve the new sensor technology and the algorithm of few-shot learning, and to adjust some parameters as a whole. At the same time, related experiments and analysis are designed for the improved algorithm to study and understand its performance. The experimental results in this paper show that the improved algorithm improves its application effect by 36.9% and is relatively more applicable than the unimproved algorithm. [ABSTRACT FROM AUTHOR]

The vehicle-road collaborative information interaction system is an emerging technology system that realizes the sharing of information between vehicles, vehicles and roads between traffic road information, and driving vehicle information. It is of positive significance for improving the urban transportation construction system and promoting urban economic development. This paper conducts intelligent research on the deep learning recognition method based on the vehicle-road collaborative information interaction system. First, this article comprehensively expounds the concept of the vehicle-road collaborative information interaction system and then introduces the specific components, functions, and applications of the system structure. Then, this article researches on deep learning recognition methods and introduces three deep learning recognition methods. They are background extraction method, YOLOv2 method, and DeepSORT method. Finally, this paper conducts simulation comparison experiments between deep learning algorithms and traditional algorithms. It evaluates the feasibility of the algorithm in the vehicle-road collaborative information interaction system in three aspects: vehicle target detection, vehicle flow identification, and emergency decision-making. The experimental results show that the value of the intersection ratio of vehicle target detection in the deep learning recognition method is 8.66% higher than that of the traditional algorithm, the recall rate is 7% higher than that of the traditional algorithm, and the vehicle flow recognition accuracy is 1.8% higher than that of the traditional algorithm. The early warning time in emergency decision-making is also shorter than that of traditional algorithms, which shows the unique superiority and feasibility of deep learning algorithms in the vehicle-road collaborative information interaction system. [ABSTRACT FROM AUTHOR]

To improve the word order ranking effect of English language retrieval, based on machine learning algorithms, this paper combines a semionline model to construct an artificial intelligence ranking model for English word order based on a semionline model and establishes a semisupervised ELM regression model. Moreover, this paper derives the mathematical model of semisupervised ELM in detail and uses FCM clustering to screen credible samples, ELM collaborative training to mark each other's samples, and the marked samples to calculate the output weights of semisupervised ELM regression. In addition, based on continuous learning of OSELMR, this paper uses confidence evaluation to screen out credible unlabeled samples, OSELM collaborative training to mark the credible samples with each other, and credible unlabeled samples to calculate the output weight of SSOSELMR. Finally, this paper designs a control experiment to analyze the model algorithm, compares and counts the parameters, and draws a statistical graph. The research results show that the model constructed in this paper is effective. [ABSTRACT FROM AUTHOR]

Book binding is the procedure of manually accumulating a book in codex format from a well-ordered pile of paper sheets, which are folded together into sections or occasionally left as a stack of individual sheets. The books undergo binding into different shapes and sizes. Numerous kinds of book bindings are available, each of which comes with its own merits and demerits. Some of them are highly durable, some of them are light-weight, and some of them are attractive. Therefore, it is needed to effectively identify and classify the shape and type of book bindings. With this motivation, this paper develops a butterfly optimization algorithm with a deep learning-enabled book binding classification (BOADL-BBC) model. The major intention of the BOADL-BBC technique is to identify and categorise three different types of book bindings from the input images, namely, hard binding, soft binding, and long-stitch binding. The proposed BOADL-BBC technique initially employs a DL-based Inception v3 model to derive useful feature vectors from the images. For effective classification of book bindings, the BOA with wavelet kernel extreme learning machine (WKELM) model can be applied. The weight and bias values involved in the WKELM model can be effectively adjusted by the use of BOA for book binding classification shows the novelty of the work. To ensure the enhanced performance of the BOADL-BBC technique, a series of simulations were carried out using a set of images that people collected on their own. The experimental results stated that the BOADL-BBC technique has obtained a maximum book binding classification accuracy of 95.56%. [ABSTRACT FROM AUTHOR]

Deep learning is a new direction in the field of machine learning, which learns the inherent laws and levels of data sample representation. The information gained during learning plays an important role in interpreting data such as text, images, and speech. This paper aims to study how to analyze and study the physical energy consumption of passers and receivers in different passing methods in football based on deep learning. This paper proposes the problem of physical energy consumption, which is based on deep learning, then elaborates on the concept of deep learning and related algorithms, and designs and analyzes the case of physical energy consumption of athletes. The experimental results showed that the average heart rhythm (184.35) of the subjects in the first and third experiments was more than twenty points higher than the average heart rhythm (159.85) of the kickers in the second and fourth experiments. Different passing styles have significantly different effects on the physical energy expenditure of players and defensive receivers. [ABSTRACT FROM AUTHOR]

In order to improve the scientificity of sports action analysis, this paper constructs a sports action analysis model based on machine learning based on the greedy algorithm and the bat algorithm. According to the structural characteristics of the model, the structure of the model is reflected in the form of face order, that is, the face neighborhood structure. Moreover, this paper judges the degree of similarity between model faces through the pros and cons of the order and applies it to the structural similarity matrix between models. In addition, this paper establishes corresponding mathematical models for the shape and structure of the model and constructs the shape similarity matrix, the surface neighborhood structure similarity matrix, and the structure similarity matrix between the source model and the target model. Finally, this paper designs and implements CAD model retrieval methods based on greedy algorithm and bat algorithm and designs experiments to compare the performance of the algorithm proposed in this paper with traditional algorithms. The result of the experiment shows that the algorithm proposed in this paper has obvious advantages in sports action analysis compared with the traditional algorithm. [ABSTRACT FROM AUTHOR]

Objective. The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data. Methods. This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax. Results. In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%. Conclusion. In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent. [ABSTRACT FROM AUTHOR]

Competitive basketball is one of the most popular sports in the world. With the development of China's sports power strategy, the national movement has strengthened the status of basketball in sports. However, China's competitive basketball ranking is not high in the world, and the analysis of the reasons should start with the management system. Therefore, this paper aims to explore the development status and influencing factors of China's competitive basketball management system under the background of deep learning. For the background of deep learning, this paper describes the application of deep learning algorithms in basketball strategy. It adopts the expert interview method for the competitive basketball management system and elaborates in detail on five aspects: target mechanism, competition mechanism, selection mechanism, market mechanism, and incentive mechanism. The experimental results of the article believe that, based on the suggestions of 10 experts, the current Chinese competitive basketball selection mechanism is the most influential factor, with a weight of 16.1%, and the smallest impact is the level of athletes, accounting for 11.4%. [ABSTRACT FROM AUTHOR]

Basketball is one of the popular sports in colleges. Basketball injuries are a common thing, and the use of machine learning and other technologies can effectively reduce basketball injuries, which should start with prevention. Nonstandard basketball movements and lack of physical coordination will not only reduce sports efficiency for athletes but also increase the probability of injury. Therefore, effective reduction and targeted prevention of nonstandard actions are of great significance to college basketball. With the development of science and technology, artificial intelligence technology is closer to our lives. Based on the machine learning platform, this paper studies basketball injuries from the perspective of the integration of sports and medicine. Research on what aspects cause college students' basketball injuries is needed for the future. Effectively preventing college students from being injured in basketball is an urgent problem in the field of sports medicine. To find the most suitable machine learning platform for college basketball injury research, this article will introduce three different methods for comparative analysis. The techniques used in the experiment in this paper are traditional BP neural network technology, SCG neural network technology, and RBF neural network technology. Through experiments, it is known that, through experiments, RBF neural network technical prediction accuracy rate is as high as 95.4%, which is a relatively good neural network algorithm for studying the basketball loss of college students. [ABSTRACT FROM AUTHOR]

With the rapid advancement of information technology, online information has been exponentially growing day by day, especially in the form of text documents such as news events, company reports, reviews on products, stocks-related reports, medical reports, tweets, and so on. Due to this, online monitoring and text mining has become a prominent task. During the past decade, significant efforts have been made on mining text documents using machine and deep learning models such as supervised, semisupervised, and unsupervised. Our area of the discussion covers state-of-the-art learning models for text mining or solving various challenging NLP (natural language processing) problems using the classification of texts. This paper summarizes several machine learning and deep learning algorithms used in text classification with their advantages and shortcomings. This paper would also help the readers understand various subtasks, along with old and recent literature, required during the process of text classification. We believe that readers would be able to find scope for further improvements in the area of text classification or to propose new techniques of text classification applicable in any domain of their interest. [ABSTRACT FROM AUTHOR]

The smart bracelet has become a hot-selling commodity, according to a daily consumption survey. Based on people's interest and concern for their health, the smart bracelet, as a design and application for achieving healthy weight loss monitoring, is quickly becoming a popular new favorite. This bracelet detects fat using the near-infrared diffuse reflection principle, with the goal of assisting people in controlling and maintaining a healthy weight. A large amount of data has been accumulated in all walks of life due to the development of the Internet network and data storage technology. As a result, the emergence of machine learning plays a critical role in the data analysis of human sports health monitoring of smart bracelets. Based on machine learning, this paper investigates the data analysis of human sports health monitoring smart bracelets. When the population index reaches 50 in the analysis of health monitoring data, the average accuracy of data mining is 86.8 percent, the average accuracy of the association rule algorithm is 85.9 percent, the average accuracy of the collaborative filtering algorithm is 84.3 percent, and the average accuracy of the machine learning algorithm is 90.1 percent in this paper. Among the four algorithms, the method presented in this paper is clearly the most effective, stable, and accurate. The system's stability and accuracy have been greatly improved by the addition of GPS-assisted and hand-up misjudgment algorithms. Because the smart bracelet is inexpensive, easy to wear, and consistent with consumer psychology, it is becoming increasingly popular to use it to monitor the human body's sports health. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore the noninvasive human-computer interaction methods that have been widely used in various fields, especially in the field of robot control. To have a deep understanding of the development of the methods, this paper employs "Mapping Knowledge Domains" (MKDs) to find research hotspots in the area to show the future potential development. Through the literature review, this paper found that there was a paradigm shift in the research of noninvasive BCI technologies for robotic control, which has occurred from early 2010 since the rapid development of machine learning, deep learning, and sensory technologies. This study further provides a trend analysis that the combination of data-driven methods with optimized algorithms and human-sensory-driven methods will be the key areas for the future noninvasive method development in robotic control. Based on the above findings, the paper provides a potential developing way of noninvasive HCI methods for related areas including health care, robotic system, and media. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm composition network from the perspective of machine learning, based on an in-depth study of related literature. At the same time, this paper examines the characteristics of music and develops a model for recognising musical emotions. Using the model's information entropy of pitch and intensity to extract the main melody track, note features are extracted from bar features. Finally, the cosine of the vector included angle is used to judge the similarity between feature vectors of several adjacent sections, allowing the music to be divided into several independent segments. The emotional model of music is used to analyze each segment's emotion. By quantifying music features, this paper classifies and quantifies music emotion based on the mapping relationship between music features and emotion. Music emotion can be accurately identified by the model. The model's emotion recognition accuracy is up to 93.78 percent, and the algorithm's recall rate is up to 96.3 percent, according to simulation results. The recognition method used in this paper has a higher recognition ability than other methods, and the emotion recognition result is more reliable. This paper can not only meet the composer's auxiliary creative needs, but it can also help intelligent music services. [ABSTRACT FROM AUTHOR]

In China, the application of corpus in language teaching, especially in English and American literature teaching, is still in the preliminary research stage, and there are various shortcomings, which have not been paid due attention by front-line educators. Constructing English and American literature corpus according to certain principles can effectively promote English and American literature teaching. The research of this paper is devoted to how to automatically build a corpus of English and American literature. In the process of keyword extraction, key phrases and keywords are effectively combined. The similarity between atomic events is calculated by the TextRank algorithm, and then the first N sentences with high similarity are selected and sorted. Based on ML (machine learning) text classification method, a combined classifier based on SVM (support vector machine) and NB (Naive Bayes) is proposed. The experimental results show that, from the point of view of accuracy and recall, the classification effect of the combined algorithm proposed in this paper is the best among the three methods. The best classification results of accuracy, recall, and F value are 0.87, 0.9, and 0.89, respectively. Experimental results show that this method can quickly, accurately, and persistently obtain high-quality bilingual mixed web pages. [ABSTRACT FROM AUTHOR]

Fatal diseases like cancer, dementia, and diabetes are very dangerous. This leads to fear of death if these are not diagnosed at early stages. Computer science uses biomedical studies to diagnose cancer, dementia, and diabetes. With the advancement of machine learning, there are various techniques which are accessible to predict and prognosis these diseases based on different datasets. These datasets varied (image datasets and CSV datasets) around the world. So, there is a need for some machine learning classifiers to predict cancer, dementia, and diabetes in a human. In this paper, we used a multifactorial genetic inheritance disorder dataset to predict cancer, dementia, and diabetes. Several studies used different machine learning classifiers to predict cancer, dementia, and diabetes separately with the help of different types of datasets. So, in this paper, multiclass classification proposed methodology used support vector machine (SVM) and K-nearest neighbor (KNN) machine learning techniques to predict three diseases and compared these techniques based on accuracy. Simulation results have shown that the proposed model of SVM and KNN for prediction of dementia, cancer, and diabetes from multifactorial genetic inheritance disorder achieved 92.8% and 92.5%, 92.8% and 91.2% accuracy during training and testing, respectively. So, it is observed that proposed SVM-based dementia, cancer, and diabetes from multifactorial genetic inheritance disorder prediction (MGIDP) give attractive results as compared with the proposed model of KNN. The application of the proposed model helps to prognosis and prediction of cancer, dementia, and diabetes before time and plays a vital role to minimize the death ratio around the world. [ABSTRACT FROM AUTHOR]

Whitelisting is a widely used method in the security field. However, due to the rapid development of the Internet, the traditional whitelisting method cannot promote the security of increasing Internet access. In recent years, with the success of machine learning in different areas, many researchers focus on the security of Internet access through machine learning methods. The most common form of machine learning is supervised learning. Supervised learning requires a large number of labeled samples, but it is difficult to obtain labeled samples in practical applications. This paper introduced an unsupervised deep learning algorithm based on seq2seq, which combined with the recurrent neural network and the autoencoder structure to realize an intelligent boundary security control mechanism. The main methods proposed in this paper are divided into two parts: data processing and modeling. In the phase of data processing, the access text table was coded with dicts, and all sequences were padded to the maximum. In the modeling phase, the network was optimized according to the principle of minimizing the reconstruction error. From the comparative experiments, the proposed method's AUC on the public data set reached 0.99, and its performance is better than several classical supervised learning algorithms, proving that the proposed method has an efficient defense against abnormal network access. [ABSTRACT FROM AUTHOR]

Since the reform and opening, China's economy has grown rapidly, and people's living standards have improved significantly. As one of the most effective ways to implement national fitness, square dance has gradually become the main lifestyle of urban communities, an important part of China's sports construction, and an important indicator to reflect the fitness of the masses and the construction of a well-off society in an all-round way. On the other hand, with the rapid development of internet of things technology, many people can use intelligent bracelets based on machine learning technology to realize motion detection. This technology is also applicable in square dance, which is of great significance to exercise and protect health. This paper first reviews the research status of the internet of things communication protocol and cloud platform, then introduces and analyzes the MQTT communication protocol and Netty high-performance network framework, and studies the integration technology of the internet of things and machine learning. Then, according to the characteristics of the internet of things, a scheme to realize data preprocessing is proposed. The value to be completed is calculated based on the correlation of other attributes corresponding to the k-nearest neighbor model (KNN) and the regression model. Finally, the machine learning algorithm is used to train the results of the three models to obtain the final filling value. The whole scheme design allows the machine learning algorithm to obtain relatively high-quality data in the internal environment. This paper designs a sports monitoring data system for square dance by combining machine learning and internet of things technology, so as to promote national fitness. [ABSTRACT FROM AUTHOR]

This paper uses feature subspace learning and cross-media retrieval analysis to construct an advertising design and communication model. To address the problems of the traditional feature subspace learning model and make the samples effectively maintain their local structure and discriminative properties after projection into the feature space, this paper proposes a discriminative feature subspace learning model based on Low-Rank Representation (LRR), which explores the local structure of samples through Low-Rank Representation and uses the representation coefficients as similarity constraints of samples in the projection space so that the projection subspace can better maintain the local nearest-neighbor relationship of samples. Based on the common subspace learning, this paper uses the extreme learning machine method to improve the cross-modal retrieval accuracy, mining deeper data features and maximizing the correlation between different modalities, so that the learned shared subspace is more discriminative; meanwhile, it proposes realizing cross-modal retrieval by the deep convolutional generative adversarial network, using unlabeled samples to further explore the correlation of different modal data and improve the cross-modal performance. The clustering quality of images and audios is corrected in the feature subspace obtained by dimensionality reduction through an optimization algorithm based on similarity transfer. Three active learning strategies are designed to calculate the conditional probability of unannotated samples around user-annotated samples in the correlation feedback process, thus improving the efficiency of cross-media retrieval in the case of limited feedback samples. The experimental results show that the method accurately measures the cross-media relevance and effectively achieves mutual retrieval between image and audio data. Through the study of cross-media advertising design and communication models based on feature subspace learning, it is of positive significance to advance commercial advertising design by guiding designers and artists to better utilize digital media technology for artistic design activities at the level of theoretical research and applied practice. [ABSTRACT FROM AUTHOR]

The idea of EAE (Ecological Aesthetic Education) is put forward on the basis of the mature development of ecological aesthetics and AE theory. Starting with EAE, establishing people's aesthetic attitude and improving people's spiritual realm will help to reverse people's hostile attitude towards nature and rebuild the harmonious relationship between man and nature. This paper studies the application optimization of AE (aesthetic education) resources in universities based on ML (machine learning) from the perspective of resource development. The recommendation algorithm based on ML is the main idea of the classification of AE resources, and the classification model of AE resources is constructed. Through deep learning, we can learn the effective features of items from the content data in advance and then transform the learned features into CF (Collaborative Filtering) target learning task. Optimize the voting mechanism of the algorithm, and compare the RF_VM (random forest with optimized voting mechanism) algorithm with the traditional RF (random forest) algorithm. Experiments show that the algorithm proposed in this paper can effectively classify texts and has high feasibility. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a new theory of solving the multitarget control problem by introducing a machine learning framework in automatic driving and implementing the acquisition of excellent drivers' knowledge. Nowadays, there still exist some core problems that have not been fully realized by the researchers in automatic driving, such as the optimal way to control the multitarget objective functions of energy saving, safe driving, headway distance control, and comfort driving. It is also challenging to resolve the networks that automatic driving is relied on and to improve the performance of GPU chips on complex driving environments. According to these problems, we developed a new theory to map multitarget objective functions in different spaces into the same one and thus introduced a machine learning framework of SDL (super deep learning) for optimal multitarget control based on knowledge acquisition. We will present in this paper the optimal multitarget control by combining the fuzzy relationship of each multitarget objective function and the implementation of excellent drivers' knowledge acquired by machine learning. Theoretically, the impact of this method will exceed that of the fuzzy control method used in the automatic train. [ABSTRACT FROM AUTHOR]

Few-Shot Learning has had a significant influence on how people live, work, and learn. Physical education is a requirement for a college diploma. Sports management systems, which focus on data collection, organization, and analysis, as well as timeliness and guidance, are one of the current challenges in the field of physical education at the country's top colleges and universities. The amount of sex in the room is minimal. Time is money when it comes to making college sports decisions, and this paper uses data from physical fitness tests to illustrate this point. Use Few-Shot Learning technology to extract relevant data from the data, allowing teachers to provide more scientific and effective guidance and suggestions to students. The design and implementation of this paper collect data from physical fitness tests in real-time using mobile edge computing, analyze the data, and display the results using machine learning technology, which mines deep features and displays analysis results, can be used to evaluate students' physical fitness. The data and information in the physical fitness analysis system are more readable and time-saving, allowing students to better understand their true level of physical fitness. Because of the results of data mining, teachers can provide more specific guidance and recommendations for each student's physical characteristics. [ABSTRACT FROM AUTHOR]

In order to construct a prediction model of sports economic operation indicators, this paper combines deep learning and ensemble learning algorithms to integrate and improve the algorithms and analyzes the principles of the LightGBM ensemble learning model and the hyperparameters of the model. Moreover, this paper obtains appropriate intelligent algorithms according to the data analysis requirements of sports economic operation. The break-even analysis method of sports event operation is to find the critical point of the program's profit and loss by analyzing the relationship between the operating cost and profit of the sports event. In addition, this paper uses deep learning and ensemble learning to comprehensively evaluate sports events, constructs a summary evaluation structure of sports items, and evaluates the model in this paper combined with experimental research. The test results verify the reliability of the model in this paper. [ABSTRACT FROM AUTHOR]

In order to improve the effect of intelligent teaching and give full play to the role of intelligent technology in modern physical education, in this paper, cloud computing and deep learning methods are used to comprehensively evaluate the teaching effect of colleges and universities, and calculate the evaluation effect and accuracy. Cloud computing and deep learning algorithm combine the teaching evaluation scale, teaching content, and characteristics to formulate teaching plans for different students and realize targeted teaching evaluation. The results show that the teaching evaluation method proposed in this paper can improve students' learning interest by about 30%, enhance learning initiative by about 20%, and the matching rate between the actual teaching effect and the expected requirements is 98%. Therefore, cloud computing and deep learning model can improve the accuracy of teaching effect evaluation in colleges and universities, provide support for the formulation of teaching evaluation schemes, and promote the development of intelligent teaching in colleges and universities. [ABSTRACT FROM AUTHOR]

With the rapid development of machine learning technology, how to use machine learning technology to empower the manufacturing industry has become a research hotspot. In order to solve the problem of product quality classification in a small sample data and imbalanced data environment, this paper proposes a data generation model called MSMOTE-GAN, which is based on Mahalanobis Synthetic Minority Oversampling Technology (MSMOTE) and Generative Adversarial Network (GAN). Among them, MSMOTE is proposed to solve the problem of the sample biased to the majority class expanded by methods such as GAN in a sample imbalanced environment. Based on the traditional SMOTE method, the sample distance measurement method is modified from Euclidean distance to Mahalanobis distance, taking into account the correlation between attributes and the influence of dimensions on the sample distance. In the data generation model, MSMOTE is used to balance the positive and negative samples in the data. GAN generates fake data with the same distribution as the original data based on a balanced data set and expands the sample size to solve the problems of overfitting and insufficient model expression ability that occur when the sample size is too small. The quality classification framework of water heater liner based on the data generation model and Random Forest is constructed, and the process of the quality classification of water heater liner under the environment of small sample data and imbalanced data is fully described. This paper compares the MSMOTE-GAN model, Bootstrap, and tableGAN on the water heater liner production line data set and the public data set. The experimental result shows that the expanded data set of the MSMOTE-GAN model can effectively improve the performance of the classification model. [ABSTRACT FROM AUTHOR]

In sports, the essence of a complete technical action is a complete information structure pattern and the athlete's judgment of the action is actually the identification of the movement information structure pattern. Action recognition refers to the ability of the human brain to distinguish a perceived action from other actions and obtain predictive response information when it identifies and confirms it according to the constantly changing motion information on the field. Action recognition mainly includes two aspects: one is to obtain the required action information based on visual observation and the other is to judge the action based on the obtained action information, but the neuropsychological mechanism of this process is still unknown. In this paper, a new key frame extraction method based on the clustering algorithm and multifeature fusion is proposed for sports videos with complex content, many scenes, and rich actions. First, a variety of features are fused, and then, similarity measurement can be used to describe videos with complex content more completely and comprehensively; second, a clustering algorithm is used to cluster sports video sequences according to scenes, eliminating the need for shots in the case of many scenes. It is difficult and complicated to detect segmentation; third, extracting key frames according to the minimum motion standard can more accurately represent the video content with rich actions. At the same time, the clustering algorithm used in this paper is improved to enhance the offline computing efficiency of the key frame extraction system. Based on the analysis of the advantages and disadvantages of the classical convolutional neural network and recurrent neural network algorithms in deep learning, this paper proposes an improved convolutional network and optimization based on the recognition and analysis of human actions under complex scenes, complex actions, and fast motion compared to post-neural network and hybrid neural network algorithm. Experiments show that the algorithm achieves similar human observation of athletes' training execution and completion. Compared with other algorithms, it has been verified that it has very high learning rate and accuracy for the athlete's action recognition. [ABSTRACT FROM AUTHOR]

To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users' psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user's multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper's accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues. [ABSTRACT FROM AUTHOR]

Human resources are the core resources of an enterprise, and the demand forecasting plays a vital role in the allocation and optimization of human resources. Starting from the basic concepts of human resource forecasting, this paper employs the backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) to analyze human resource needs and determine the key elements of the company's human resource allocation through predictive models. With historical data as reference, the forecast value of current human resource demand is obtained through the two types of neural networks. Based on the prediction results, the company managers can carry out targeted human resource planning and allocation to improve the efficiency of enterprise operations. In the experiment, the actual human resource data of a certain company are used as the experimental basic samples to train and test the two types of machine learning tools. The experimental results show that the method proposed in this paper can effectively predict the number of personnel required and can support the planning and allocation of human resources. [ABSTRACT FROM AUTHOR]

As the core of artificial intelligence, machine learning has strong application advantages in multi-criteria intelligent evaluation and decision-making. The level of sustainable development is of great significance to the safety evaluation of coal mining enterprises. BP neural network is a classical algorithm model in machine learning. In this paper, the BP neural network is applied to the sustainable development level decision-making and safety evaluation of coal mining enterprises. Based on the analysis of the evaluation method for sustainable development of coal enterprises, the evaluation index system of sustainable development of coal enterprises is established, and a multi-layer forward neural network model based on error backpropagation algorithm is constructed. Based on the system theory of man, machine, environment, and management, and taking the four single elements and the whole system in a coal mine as the research object, this paper systematically analyzes and studies the evaluation and continuous improvement of coal mine intrinsic safety. The BP neural network evaluation model is used to analyze and study the intrinsic safety of coal mines, the shortcomings of the intrinsic safety construction of coal mines are found, and then improvement measures are put forward to effectively promote the safe production of coal mines and finally realize the intrinsic safety goal of the coal mine. [ABSTRACT FROM AUTHOR]

The rapid development of social economy not only increases people's living pressure but also reduces people's health. Looking for a healthy development prediction model has become a domestic concern. Based on the analysis of the influencing factors of health development, this paper looks for a model to predict the development of public health, so as to improve the accuracy of health development prediction. In this paper, the linear sequential extreme learning machine algorithm can be used to evaluate the health status of a large number of data, analyze the differences of each evaluation index, and construct the analysis model of health status. Therefore, this paper introduces rough set theory into linear sequential extreme learning machine algorithm. Rough set can analyze the double analysis of evaluation scheme, predict the health development of different individuals, and improve the evaluation accuracy of mass health evaluation. The simulation results show that the improved line sequential extreme learning machine algorithm can accurately analyze the mass health and meet the needs of different individuals' health evaluation. [ABSTRACT FROM AUTHOR]

Cylinder liners are important to automobile engines. The appearance quality will directly affect the life and safety of the engines. At present, the appearance quality inspection of cylinder liners mainly relies on manual visual judgment, which is easily affected by the subjective factors of inspectors. This paper studies improved machine vision to realize surface defect detection. It proposes the improvement of the attention mechanism and a feature fusion method to locate and classify the defect. Experiments show that the method proposed in this paper has improved both accuracy and speed, and it can detect defects in production and realize industrialization. At the same time, the method studied in this paper has the value of popularization and application for appearance defect detection in other fields. [ABSTRACT FROM AUTHOR]

The diagnosis of thyroid nodules at an early stage is a challenging task. Manual diagnosis of thyroid nodules is labor-intensive and time-consuming. Meanwhile, due to the difference of instruments and technical personnel, the original thyroid nodule ultrasound images collected are very different. In order to make better use of ultrasound image information of thyroid nodules, some image processing methods are indispensable. In this paper, we developed a method for automatic thyroid nodule classification based on image enhancement and deep neural networks. The selected image enhancement method is histogram equalization, and the neural networks have four-layer network nodes in our experiments. The dataset in this paper consists of thyroid nodule images of 508 patients. The data are divided into 80% training and 20% validation sets. A comparison result demonstrates that our method can achieve a better performance than other normal machine learning methods. The experimental results show that our method has achieved 0.901961 accuracy, 0.894737 precision, 1 recall, and 0.944444 F1-score. At the same time, we also considered the influence of network structure, activation function of network nodes, number of training iterations, and other factors on the classification results. The experimental results show that the optimal network structure is 2500-40-2-1, the optimal activation function is logistic function, and the best number of training iterations is 500. [ABSTRACT FROM AUTHOR]

Healthcare is predominantly regarded as a crucial consideration in promoting the general physical and mental health and well-being of people around the world. The amount of data generated by healthcare systems is enormous, making it challenging to manage. Many machine learning (ML) approaches were implemented to develop dependable and robust solutions to handle the data. ML cannot fully utilize data due to privacy concerns. This primarily happens in the case of medical data. Due to a lack of precise clinical data, the application of ML for the same is challenging and may not yield desired results. Federated learning (FL), which is a recent development in ML where the computation is offloaded to the source of data, appears to be a promising solution to this problem. In this study, we present a detailed survey of applications of FL for healthcare informatics. We initiate a discussion on the need for FL in the healthcare domain, followed by a review of recent review papers. We focus on the fundamentals of FL and the major motivations behind FL for healthcare applications. We then present the applications of FL along with recent state of the art in several verticals of healthcare. Then, lessons learned, open issues, and challenges that are yet to be solved are also highlighted. This is followed by future directions that give directions to the prospective researchers willing to do their research in this domain. [ABSTRACT FROM AUTHOR]

This paper discusses the machine learning effect on healthcare and the development of an application named "Medicolite" in which various modules have been developed for convenience with health-related problems like issues with diet. It also provides online doctor appointments from home and medication through the phone. A healthcare system is "Smart" when it can decide on its own and can prescribe patients life-saving drugs. Machine learning helps in capturing data that are large and contain sensitive information about the patients, so data security is one of the important aspects of this system. It is a health system that uses trending technologies and mobile internet to connect people and healthcare institutions to make them aware of their health condition by intelligently responding to their questions. It perceives information through machine learning and processes this information using cloud computing. With the new technologies, the system decreases the manual intervention in healthcare. Every single piece of information has been saved in the system and the user can access it any time. Furthermore, users can take appointments at any time without standing in a queue. In this paper, the authors proposed a CNN-based classifier. This CNN-based classifier is faster than SVM-based classifier. When these two classifiers are compared based on training and testing sessions, it has been found that the CNN has taken less time (30 seconds) compared to SVM (58 seconds). [ABSTRACT FROM AUTHOR]

With the rapid development of DNA high-throughput testing technology, there is a high correlation between DNA sequence variation and human diseases, and detecting whether there is variation in DNA sequence has become a hot research topic at present. DNA sequence variation is relatively rare, and the establishment of DNA sequence sparse matrix, which can quickly detect and reason fusion variation point, has become an important work of tumor gene testing. Because there are differences between the current comparison software and mutation detection software in detecting the same sample, there are errors between the results of derivative sequence comparison and the detection of mutation. In this paper, SNP and InDel detection methods based on machine learning and sparse matrix detection are proposed, and VarScan 2, Genome Analysis Toolkit (GATK), BCFtools, and FreeBayes are compared. In the research of SNP and InDel detection with intelligent reasoning, the experimental results show that the detection accuracy and recall rate are better when the depth is increasing. The reasoning fusion method proposed in this paper has certain advantages in comparison effect and discovery in SNP and InDel and has good effect on swelling and pain gene detection. [ABSTRACT FROM AUTHOR]