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Recent developments in deep learning have enabled data-driven algorithms that can reach human-level performance and beyond. The development and deployment of medical image analysis methods have several challenges, including data heterogeneity due to population diversity and different device manufacturers. In addition, more input from experts is required for a reliable method development process. While the exponential growth in clinical imaging data has enabled deep learning to flourish, data heterogeneity, multi-modality, and rare or inconspicuous disease cases still need to be explored. Endoscopy being highly operator-dependent with grim clinical outcomes in some disease cases, reliable and accurate automated system guidance can improve patient care. Most designed methods must be more generalisable to the unseen target data, patient population variability, and variable disease appearances. The paper reviews recent works on endoscopic image analysis with artificial intelligence (AI) and emphasises the current unmatched needs in this field. Finally, it outlines the future directions for clinically relevant complex AI solutions to improve patient outcomes. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology and information technology, the posture of volleyball training is becoming increasingly strict. By analyzing the dynamic training posture monitoring algorithm, the posture information of the human body can be directly obtained, which enables more efficient management of volleyball sports training. This paper aims to study how to monitor volleyball training posture and give suggestions based on machine learning in the context of artificial intelligence. The traditional method of manually detecting volleyball training postures is too subjective and cannot be used to judge the movements. Therefore, this paper proposes an algorithm for human posture monitoring and studies human posture recognition. Human gesture recognition has been widely used in many fields. The experimental results in this paper show that the corrected serve deviation rate of five volleyball players is 13.1% at the highest and 11.3% at the lowest after the traditional manual visual monitoring. The highest error is 0.70 m and the lowest is 0.63 m. The overall error is high. The corrected service deviation rate of the machine learning-based attitude monitoring algorithm is 3.5% at the highest and 2.7% at the lowest. The highest error is 0.24 m and the lowest is 0.19 m. The overall error is much lower than the former. This also shows that the posture monitoring algorithm based on machine learning can effectively detect the movement of volleyball players. This enables athletes to correct their mistakes in a timely manner, improve training efficiency, and improve their own strength. [ABSTRACT FROM AUTHOR]

Machine learning techniques aim to mimic the human ability to automatically learn how to perform tasks through training examples. They have proven capable of tasks such as prediction, learning and adaptation based on experience and can be used in virtually any scientific application, ranging from biomedical, robotic, to business decision applications, and others. However, the lack of domain knowledge for a particular application can make feature extraction ineffective or even unattainable. Furthermore, even in the presence of pre-processed datasets, the iterative process of optimizing Machine Learning parameters, which do not translate from one domain to another, maybe difficult for inexperienced practitioners. To address these issues, we present in this paper a Vectorized Automated ML Pre-processIng and post-pRocEssing framework, approximately named (VAMPIRE), which implements feature extraction algorithms capable of converting large time-series recordings into datasets. Also, it introduces a new concept, the Activation Engine, which is attached to the output of a Multi Layer Perceptron and extracts the optimal threshold to apply binary classification. Moreover, a tree-based algorithm is used to achieve multi-class classification using the Activation Engine. Furthermore, the internet of things gives rise to new applications such as remote sensing and communications, so consequently applying Machine Learning to improve operation accuracy, latency, and reliability is beneficial in such systems. Therefore, all classifications in this paper were performed on the edge in order to reach high accuracy with limited resources. Moreover, forecasts were applied on three unrelated biomedical datasets, and on two other pre-processed urban and activity detection datasets. Features were extracted when required, and training and testing were performed on the Raspberry Pi remotely, where high accuracy and inference speed were achieved in every experiment. Additionally, the board remained competitive in terms of power consumption when compared with a laptop which was optimized using a Graphical Processing Unit. [ABSTRACT FROM AUTHOR]

In this paper, I propose a conceptual framework for law and artificial intelligence (AI) that is based on ideas derived from systems and control theory. The approach considers the relationship between the input to an AI-controlled system and the system's output, which may affect events in the real-world. The approach aims to add to the current discussion among legal scholars and legislators on how to regulate AI, which focuses primarily on how the output, or external behavior of a system, leads to actions that may implicate the law. The goal of this paper is to show that not only is the systems output an important consideration for law and AI but so too is the relationship between the systems input to its desired output, as mediated through a feedback loop (and other control variables). In this paper, I argue that ideas derived from systems and control theory can be used to provide a conceptual framework to help understand how the law applies to AI, and particularly, to algorithmically based systems. [ABSTRACT FROM AUTHOR]

Musical choreography is usually completed by professional choreographers, which is very professional and time-consuming. In order to realize the intelligent choreography of musical, based on the mixed density network (MDN), this paper generates the dance matching with the target music through three steps: motion generation, motion screening, and feature matching. The choreography results in this paper have a high degree of matching with music, which makes it possible for the development of motion capture technology and artificial intelligence and computer automatic choreography based on music. In the process of motion generation, the average value of Gaussian model output by MDN is used as the bone position and the consistency of motion is measured according to the change rate of joint velocity in adjacent frames in the process of motion selection. Compared with the existing studies, the dance generated in this paper has improved in motion coherence and realism. In this paper, a multilevel music and action feature matching algorithm combining global feature matching and local feature matching is proposed. The algorithm improves the unity and coherence of music and action. The algorithm proposed in this paper improves the consistency and novelty of movement, the compatibility with music, and the controllability of dance characteristics. Therefore, the algorithm in this paper technically changes the way of artistic creation and provides the possibility for the development of motion capture technology and artificial intelligence. [ABSTRACT FROM AUTHOR]

To study the classification and evolution of key technologies in the transportation field, the data of 36 authoritative SCI journals in the transportation field were collected from the Web of Science core collection database from 2001 to 2020. Based on the bibliometric method, this study used Python to process and visualize data, combined with bibliometric software VOSviewer to assist data visualization. Firstly, a preprocessing data algorithm was designed to deduplicate the collected data, merge synonyms, and extract key technologies. Then the paper records that contained the key technology lexicon were filtered out. Next, the annual number of publications and the distribution of key technologies over time were counted. The least squares method was used to fit the distribution of the annual proportion of the publications, and the slope k1 of the fitted linear regression equation was used to determine the research interest trend of key technologies. The key technologies were divided into "hot technology," "cold technology," and "other technologies," according to the research heat trend. In order to further explore the research hotspots, the least squares method was also used to fit the citations of all technologies to obtain the slope k2. We use the Gaussian mixture model (GMM) algorithm to cluster k1 and k2 of each technology. As a result, the 144 technologies were divided into 13 super-key technologies, 60 key technologies, 59 relative key technologies, and 12 lower-key technologies. Then, the evolution of key technologies was analyzed from two perspectives of weighted evolution and cumulative evolution. And the technology evolution trend in the transportation field in the past 20 years was explored. Finally, the cooccurrence clustering method was adopted to divide key transportation technologies into five categories: vehicle technology and control, optimization algorithms and simulation techniques, artificial intelligence and big data, Internet of Things and computing, and communication technology. The research results can provide references for different people in the transportation field, including but not limited to researchers, journal editors, and funding agencies. [ABSTRACT FROM AUTHOR]

This paper presents an indepth analysis and research on the quantitative design of fine art images through artificial intelligence algorithms. A CycleGAN-based network model for automatic generation of sketches of fine art images is constructed to extract the edge and contour features of fine art images. The network uses 512 × 1024 high-resolution art images as input and Pitchman as a discriminator. To further enhance the sketch generation effect, a bilateral filtering algorithm is added to the generator model for noise reduction, and then a K -means algorithm is used for color quantization to solve the problem of cluttered lines in the generated sketches. The experimental results show that the network model can effectively realize the automatic generation of art image sketches and can retain the detailed part of the costume information well. A rendering platform is built to realize the application of art image generation algorithms and coloring algorithms. The platform integrates the functions of image preprocessing, sketch generation, and sketch coloring, demonstrates the results of the main research content of this paper, and finally increases the interest of the system through the rendering function of the art image grid, which further improves the practicality of the platform. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence has become an important discipline in the field of computer science. Students, in the absence of basic prior knowledge, may have difficulty tracking materials when they first encounter complex and abstract artificial intelligence algorithms. Numerous researchers and educators point out that the use of simulation systems and software tools to illustrate the dynamic behavior of the algorithm can prove to be an effective solution. The introduction and adoption of new technologies in learning and teaching has evolved rapidly. This conceptual review paper aims to explore the emergence of innovative educational technologies in the teaching and learning of artificial intelligence. The aim of this paper is to analyze the existing representative educational tools for learning topics in the field of artificial intelligence to highlight their characteristics and areas they cover, so that readers can more easily draw conclusions about the possible use of some of the analyzed systems. [ABSTRACT FROM AUTHOR]

Ensuring the transparency of machine learning models is vital for their ethical application in various industries. There has been a concurrent trend of distributed machine learning designed to limit access to training data for privacy concerns. Such models, trained over horizontally or vertically partitioned data, present a challenge for explainable AI because the explaining party may have a biased view of background data or a partial view of the feature space. As a result, explanations obtained from different participants of distributed machine learning might not be consistent with one another, undermining trust in the product. This paper presents an Explainable Data Collaboration Framework based on a model-agnostic additive feature attribution algorithm (KernelSHAP) and Data Collaboration method of privacy-preserving distributed machine learning. In particular, we present three algorithms for different scenarios of explainability in Data Collaboration and verify their consistency with experiments on open-access datasets. Our results demonstrated a significant (by at least a factor of 1.75) decrease in feature attribution discrepancies among the users of distributed machine learning. The proposed method improves consistency among explanations obtained from different participants, which can enhance trust in the product and enable ethical application in various industries. [ABSTRACT FROM AUTHOR]

AI technology is capturing the African imaginations as a gateway to progress and prosperity. There is a growing interest in AI by different actors across the continent including scientists, researchers, humanitarian and aid organizations, academic institutions, tech start-ups, and media organizations. Several African states are looking to adopt AI technology to capture economic growth and development opportunities. On the other hand, African researchers highlight the gap in regulatory frameworks and policies that govern the development of AI in the continent. They argue that this could lead to AI technology exacerbating problems of inequalities and injustice in the continent. However, most of the literature on AI ethics is biased toward Euro-American perspectives and lack the understanding of how AI development is apprehended in the Global South, and particularly Africa. Drawing on the case study of the first African Master's in Machine Intelligence program, this paper argues for looking beyond the question of ethics in AI and examining AI governance issues through the analytical lens of the raciality of computing and the political economy of technoscience to understand AI development in Africa. By doing so, this paper seeks a different theorization for AI ethics from the South that is based on lived experiences of those in the margins and avoids the framings of technological futures that simplistically pathologize or celebrate Africa. [ABSTRACT FROM AUTHOR]

With the continued development of artificial intelligence technology, unmanned surface vehicles (USVs) have attracted the attention of countless domestic and international specialists and academics. In particular, path planning is a core technique for the autonomy and intelligence process of USVs. The current literature reviews on USV path planning focus on the latest global and local path optimization algorithms. Almost all algorithms are optimized by concerning metrics such as path length, smoothness, and convergence speed. However, they also simulate environmental conditions at sea and do not consider the effects of sea factors, such as wind, waves, and currents. Therefore, this paper reviews the current algorithms and latest research results of USV path planning in terms of global path planning, local path planning, hazard avoidance with an approximate response, and path planning under clustering. Then, by classifying USV path planning, the advantages and disadvantages of different research methods and the entry points for improving various algorithms are summarized. Among them, the papers which use kinematic and dynamical equations to consider the ship's trajectory motion planning for actual sea environments are reviewed. Faced with multiple moving obstacles, the literature related to multi-objective task assignment methods for path planning of USV swarms is reviewed. Therefore, the main contribution of this work is that it broadens the horizon of USV path planning and proposes future directions and research priorities for USV path planning based on existing technologies and trends. [ABSTRACT FROM AUTHOR]

Statistical machine learning usually achieves high-accuracy models by employing tens of thousands of examples. By contrast, both children and adult humans typically learn new concepts from either one or a small number of instances. The high data efficiency of human learning is not easily explained in terms of standard formal frameworks for machine learning, including Gold's learning-in-the-limit framework and Valiant's probably approximately correct (PAC) model. This paper explores ways in which this apparent disparity between human and machine learning can be reconciled by considering algorithms involving a preference for specificity combined with program minimality. It is shown how this can be efficiently enacted using hierarchical search based on identification of certificates and push-down automata to support hypothesizing compactly expressed maximal efficiency algorithms. Early results of a new system called DeepLog indicate that such approaches can support efficient top-down construction of relatively complex logic programs from a single example. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'. [ABSTRACT FROM AUTHOR]

Based on the rapid development of big data, cloud computing, Internet of things and other technologies in recent years, intelligent hardware devices has been applied to all aspects of life. Under this background, some scholars have put forward relevant concepts such as "Smart Life". In the field of mass sports life, through the development and application of science and technology, there has been new changes related to the application of neural network algorithm technology and intelligent hardware devices. Therefore, artificial intelligence wearable devices based on wearable technology came into being. This paper analyzes the application of this device in mass sports activities. Then, this paper describes the key research technologies of motion data processing based on neural network algorithm, including: depth frame differential convolution neural network structure, motion data extraction method, human motion signal processing algorithm, etc.; then it analyzes the action recognition and interaction system design based on Intelligent wearable devices. Finally, it analyzes the recognition results of human action system, the accuracy of human action recognition system and the factors that affect the performance of the recognition system. It is concluded that the artificial intelligent wearable devices designed in this paper can be well used in popular sports activities. Finally, it introduces the research on the evaluation strategy of popular sports activities based on artificial intelligence, and hopes that this equipment can help public sports activities. This paper studies the neural network algorithm and applies it to the design process of artificial intelligence wearable devices, which promotes the development of mass sports activity evaluation. [ABSTRACT FROM AUTHOR]

Digital Image Processing has dominated Digital Signal Processing at the cost of more memory, resources, and high computational power. In image processing, filtering transformations and other operations need complex multiplications, and the multiplier is one of the most resources consuming elements. Recently, mitigating the multiplier complexity in the digital signal processing (DSP) algorithms sigma-delta modulation based general purpose and adaptive DSP algorithms are developed in MATLAB and compared with its counterpart multibit algorithms for functionality and area-performance-power in FPGA. The contemporary multiplier algorithms are also optimized to overcome the multiplier complexity challenge as computation becomes simple and fast. This paper extends the reported work by investigating the sigma-delta modulation approaches for developing a computationally efficient low-power image processing algorithm. The proposed model is designed, developed, and simulated in MATLAB. The simulation results are analyzed using SNR, MSE, and Peak SNR. The simulation results show that the proposed system can better mitigate the noise effect, making it robust for noisy environment. [ABSTRACT FROM AUTHOR]

Today is the era of ultra-age technology and practices for the betterment of the society. Drone is the Unmanned Aerial Vehicle (UAV), which needs a path planning to reach up to the target. There are two basic modes for use of drone in case of military/surveillance: first is attack mode and defensive mode. Hence, this paper focuses on defensive mode as a scope of the proposed study. This paper provides significance of drone surveillance, a new artificial intelligence strategy to develop a predictive model based on the path planning. Further, based on the drone dataset, the UAV travel graph can be predicted and tested with a recursive machine learning algorithm. This strategy can be clubbed as an image path using deep learning algorithm also but to ensure the graph-based training and testing, the proposed research will use CNN algorithm for comparative analysis of simulated path's plan coordinates. This further can be developed as a human-machine interface module. [ABSTRACT FROM AUTHOR]

Blood banks play a critical role in ensuring a steady supply of safe blood for medical procedures. However, donor recruitment and retention pose significant challenges to the sustainability of blood banks. This study proposes an AI-enabled decision-support system to optimize donor recruitment and retention strategies in blood banks. The system leverages machine learning algorithms to analyze historical donor data, demographic information, and external factors to predict donor behavior and identify potential strategies for improving recruitment and retention. By incorporating AI into decision-making processes, blood banks can make data-driven decisions, enhance the efficiency of donor management, and allocate resources effectively. This paper presents the methodology used to develop the AI-enabled system and discusses its potential benefits and implications for blood bank operations. Experimental results demonstrate the effectiveness of the system in identifying successful recruitment and retention strategies. Overall, the research offers valuable insights into the application of AI in blood bank management, ultimately leading to more sustainable and efficient donor recruitment and retention practices. [ABSTRACT FROM AUTHOR]

Chat generative pre-trained transformer (ChatGPT) is a chatbot developed by OpenAI that answers questions in a human-like manner. ChatGPT is a GPT language model that understands and responds to natural language created using a transformer, which is a new artificial neural network algorithm first introduced by Google in 2017. ChatGPT can be used to identify research topics and proofread English writing and R scripts to improve work efficiency and optimize time. Attempts to actively utilize generative artificial intelligence (AI) are expected to continue in clinical settings. However, ChatGPT still has many limitations for widespread use in clinical research, owing to AI hallucination symptoms and its training data constraints. Researchers recommend avoiding scientific writing using ChatGPT in many traditional journals because of the current lack of originality guidelines and plagiarism of content generated by ChatGPT. Further regulations and discussions on these topics are expected in the future. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. Combining the physical training of deep learning will bring good practical value. Physical training has different effects on people's body shape, physical function, and physical quality. It is mainly reflected in the changes of relevant physical indicators after physical training. Therefore, the purpose of this article is to study the method of evaluating the impact of sports training on physical indicators based on deep learning. This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm to evaluate the impact of physical index. The experimental results show that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. Firstly, in the angular motion, the accuracy of the mean algorithm is 0.4, the accuracy of the variance algorithm is 0.2, the accuracy of the RFE algorithm is 0.4, and the accuracy of the DLA algorithm is 0.6. Similarly, in foot racing and skill sports, the accuracy of the algorithm proposed in this paper is significantly higher than that of other algorithms. Therefore, the method proposed in this paper is more effective in the evaluation of the impact of physical training on physical indicators. [ABSTRACT FROM AUTHOR]

With the development of deep learning, gesture recognition systems based on the neural network have become quite advanced, but the application effect in the elderly is not ideal. Due to the change of the palm shape of the elderly, the gesture recognition rate of most elderly people is only about 70%. Therefore, in this paper, an intelligent gesture error correction algorithm based on game rules is proposed on the basis of the AlexNet. Firstly, this paper studies the differences between the palms of the elderly and young people. It also analyzes the misread gesture by using the probability statistics method and establishes a misread-gesture database. Then, based on the misreading-gesture library, the maximum channel number of different gestures in the fifth layer is studied by using the similar curve algorithm and the Pearson algorithm. Finally, error correction is completed under the game rule. The experimental results show that the gesture recognition rate of the elderly can be improved to more than 90% by using the proposed intelligent error correction algorithm. The elderly-accompanying robot can understand people's intentions more accurately, which is well received by users. [ABSTRACT FROM AUTHOR]

With the continuous mining and gradual reduction of shallow deposits, deep prospecting has become a new global prospecting trend. In addition, with the development of artificial intelligence, deep learning provides a favorable means for geological big data analysis. This paper, researches the No. II Orebody of the Xiongcun deposit. First, based on previous research results and metallogenic regularity, prospecting information, namely, lithology, Au-Ag-Cu chemical elements and wall rock alteration is extracted, and the block model is established by combining the Kriging interpolation structure. Second, the datasets are divided into dataset I and dataset II according to "randomness" and "depth". Third, deep prospecting prediction models based on deep neural networks (DNN) and the convolutional neural networks (CNN) is constructed, and the model parameters are optimized. Finally, the models are applied to the deep prediction of the Xiongcun No. II Orebody. The results show that the accuracy rate and recall rate of the prediction model based on the DNN algorithm are 96.15% and 89.23%, respectively, and the AUC is 96.39%, which are higher values than those of the CNN algorithm, indicating that the performance of the prediction model based on the DNN algorithm is better. The accuracy of prediction model based on dataset I is higher than that of dataset II. The accuracy of deep metallogenic prediction based on the DNN algorithm is approximately 89%, that based on the CNN is approximately 87%, and that based on prospecting information method is approximately 61.27%. The prediction results of the DNN algorithm are relatively consistent in the spatial location and scale of the orebody. Therefore, based on the work done in this paper, it is feasible to use a deep learning method to carry out deep mineral prediction. [ABSTRACT FROM AUTHOR]

At present, economic business activities occur to the accounting element confirmation link of the accounting information system (AIS), which still occupies a large amount of manpower and material resources of the enterprise to generate information. This has been restricting the development of modern AIS. Based on this, first, this paper studies the influence of the knowledge economy on accounting innovation and in the innovation economy. The process of confirmation for accounting elements in economic business, under the traditional AIS, is analyzed. Then, the model of the accounting element confirmation, i.e., BPNN, is constructed by combining the backpropagation (BP) neural network (NN) theory with the artificial intelligence (AI) algorithm. Finally, we simulate the confirmation process of accounting elements based on the economic business data of specific online stores. The experimental outcomes illustrate that under the proposed BPNN model, the output value of the accounting entry is also increasing and has been in the interval [0, 0.14] with the continuous increase in the input value of economic business activities. Moreover, the overall simulation error of economic business activity data does not exceed 0.3% in the simulation test of the proposed accounting business data confirmation model based on the BPNN algorithm. The empirical outcomes indicate that the model has high accuracy and reliability. The purpose is to realize the identification of business events by machines and complete the automatic confirmation of accounting elements in the back-end economic business of online stores. This paper provides new ideas for realizing the overall intelligence of the AIS. [ABSTRACT FROM AUTHOR]

This paper presents a real-time object-based 3D change detection method that is built around the concept of semantic object maps. The algorithm is able to maintain an object-oriented metric-semantic map of the environment and can detect object-level changes between consecutive patrol routes. The proposed 3D change detection method exploits the capabilities of the novel ZED 2 stereo camera, which integrates stereo vision and artificial intelligence (AI) to enable the development of spatial AI applications. To design the change detection algorithm and set its parameters, an extensive evaluation of the ZED 2 camera was carried out with respect to depth accuracy and consistency, visual tracking and relocalization accuracy and object detection performance. The outcomes of these findings are reported in the paper. Moreover, the utility of the proposed object-based 3D change detection is shown in real-world indoor and outdoor experiments. [ABSTRACT FROM AUTHOR]

In recent years, people's living standards are getting higher and higher, and life pressure is also increasing, and there are also many problems in eating habits. This is also the direct cause of colon cancer. The aim of this paper was to investigate whether anesthetic drugs could positively affect the proliferation and apoptosis of colon cancer cells. In this paper, the significance of anesthetic drugs is proposed, and an artificial neural network algorithm based on artificial intelligence is proposed. It is well known that artificial neural networks play an important role in medicine. The experimental results of this paper show that the incidence of colon cancer in 2020 will be in the range of 5%-35%, and the incidence of colon cancer in 2021 will be in the range of 7%-30%. While colon cancer rates in 2021 do not appear to be as high as colon cancer rates in 2020, they are generally much higher than colon cancer rates in 2020. It can be seen that as the population ages, the number of colon cancer patients is increasing due to the lack of emphasis on health. This also means that the incidence of colon cancer is getting higher and higher, and traditional drug chemotherapy has been unable to play a good role in inhibiting the proliferation of colon cancer cells. Therefore, this paper investigated the effects of anesthetic drugs on the proliferation and apoptosis of human colon cancer cells. [ABSTRACT FROM AUTHOR]

The occurrence of forest fires causes serious damage to ecological diversity and the safety of people's property and life. However, due to the complex forest environment, the changeable shape of forest fires, and the uncertainty of flame color and texture, forest fire detection becomes very difficult. Traditional image processing methods rely heavily on artificial features and are not generally applicable to different forest fire scenes. In order to solve the problem of inaccurate forest fire recognition caused by the manual extraction of features, some scholars use deep learning technology to adaptively learn and extract forest fire features, but they often use a single target detection model, and their lack of learning and perception makes it difficult for them to accurately identify forest fires in a complex forest fire environment. Therefore, in order to overcome the shortcomings of the manual extraction of features and achieve a higher accuracy of forest fire recognition, this paper proposes an algorithm based on weighted fusion to identify forest fire sources in different scenarios, fuses two independent weakly supervised models Yolov5 and EfficientDet, completes the training and prediction of data sets in parallel, and uses the weighted boxes fusion algorithm (WBF) to process the prediction results to obtain the fusion frame. Finally, the model is evaluated by Microsoft COCO standard. Experimental results show that compared with Yolov5 and EfficientDet, the proposed Y4SED improves the detection performance by 2.5% to 4.5%. The fused algorithm proposed in this paper has better feature extraction ability, can extract more forest fire feature information, and better balances the recognition accuracy and complexity of the model, which provides a reference for forest fire target detection in the real environment. [ABSTRACT FROM AUTHOR]

Game data collection system is a tool used to collect the behavior data of users about the game. It can be used for data analysis of user behavior so that game manufacturers can keep abreast of market dynamics and popular trends, and they also can have a deeper understanding of the behavioral habits and psychology of player user groups. The defects of the current data acquisition system include that the data are not encrypted. The network transmission efficiency is relatively low. The acquisition speed is slow, and the settings cannot be dynamically changed. This paper proposes to study how to enhance the acquisition ability and improve the analysis efficiency in the design of data acquisition system for solving these problems. Therefore, on the basis of artificial intelligence algorithm, this paper designs a game data collection system by using artificial neural network algorithm, support vector algorithm, and cluster analysis algorithm, which solves the basic problem of slow data collection in current data collection and plays a role in improving the efficiency of network transmission. The experimental results in this paper show that when the number of data is more than 300, the time-consuming time reaches more than 68 ms. When the number of written data is more than 300, it takes more than 181 ms. When the number of deleted data is more than 300, it takes more than 236 ms. From the above data, it shows that the designed game data collection system is rapid and efficient. [ABSTRACT FROM AUTHOR]

As computer and multimedia technology advances and as the variety of knowledge display forms expands, there are more ways for people to obtain knowledge and information. The best course in musicology is voice performance. A crucial issue is how to select and categorize valuable curriculum materials of varying quality levels. This paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning technology and tests it on the corresponding dataset, beginning with multidimensional association rule mining technology and the premise that multimedia data contain numerous useful characteristics. Experiments demonstrate the classification precision and data integration capacity of the proposed algorithm. Vocal performance is the most engaging course in musicology. To cultivate corresponding talents, we can rely on college and university offline courses and the rapidly developing multimedia technology to train talents online. An important topic is how to efficiently select and classify curriculum materials of varying quality in order to provide them to students with different learning needs. We can achieve the automatic classification of the course content by leveraging the superior learning capabilities of artificial intelligence and machine learning technology. Consequently, this paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning-related technology and conducts an experimental test on the corresponding dataset, beginning with multidimensional association rule mining technology and the perspective that multimedia information itself contains a large number of useful characteristics. Experiments demonstrate that the proposed algorithm has a high level of classification accuracy and data integration capability. [ABSTRACT FROM AUTHOR]

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