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College students are in the stage of transitioning from teenagers to adults, and there are many problems that need to be adapted and adjusted in life, learning, and interacting with others. If these issues are not handled properly, it would lead to many mental health problems that affect normal learning and life. The paper use the data mining algorithm of Big data. Through systematic intelligent evaluation, it can gain an overall understanding of the mental status of students. For students, it is effective to have a clear understanding of their mental health status, and to communicate with their home and school in a timely manner regarding their psychological problems. Early treatment of mental health can be more effective in completing rehabilitation; For universities, they can have a clear understanding of each student's psychological situation to avoid some difficult situations. At the same time, universities can timely intervene and treat students with psychological problems. This paper use data mining algorithms to build an intelligent evaluation system for mental health. Through research, it is found that the accuracy of this method is 95% at the lowest and 97% at the highest. This method can comprehensively and objectively describe the changing characteristics of college students' mental health, and the results of mental health intelligence assessment are stable, providing valuable reference for their mental health management. [ABSTRACT FROM AUTHOR]

Electricity transmission line is the most significant way of power transmission. Regular detection of it can find and eliminate its defects and hidden dangers in time and prevent major accidents, which is of great significance to the power system. In order to find the problems in the electricity transmission line in time, this paper applied the data mining algorithm to the intelligent detection method of electricity transmission line equipment defects. An electricity transmission line equipment defect intelligent detection and monitoring system was constructed, and the differences between clustering analysis image recognition technology in data mining algorithms and the XGBoost algorithm were analyzed. The results showed that compared with using XGBoost algorithms, the highest accuracy rate of the intelligent detection method of electricity transmission line equipment defects using data mining algorithm in the detection results was 98 %, which was generally higher than that of XGBoost algorithms, and could reduce the consumption of time. From the perspective of replication rate, the overall average value of XGBoost algorithms was 52.38 % and the overall average value of data mining algorithms was 7.63 %. The replica rate of data mining algorithm was much lower than that of XGBoost algorithm and the performance of fault signal detection was better. Therefore, the application of data mining algorithm to the intelligent detection method of electricity transmission line equipment defects can be more suitable, thus significantly improving the efficiency of all aspects. At the same time, the method has the advantages of simple operation, fast, reliable and not affected by region.

When processing the lost data of web learning resource information flow, the noise in the data signal cannot be eliminated, resulting in inaccurate detection of the lost data of web learning resource information flow in the later stage. Therefore, a data mining algorithm is proposed based on weighted depth forest for web learning resource information flow loss. Based on building a brand-driven Web data acquisition model to collect data, this method uses clustering analysis technology to extract the lost data feature information of web learning resource information flow. It carries out wavelet threshold denoising on it. According to the characteristics of lost data, the lost data mining of web learning resource information flow is completed. Experimental results show that the proposed algorithm has a low error rate, high accuracy, high labour intensity, high efficiency and high performance. [ABSTRACT FROM AUTHOR]

The existing forensics systems are developing in a more and more perfect direction in terms of forensic steps, legal constraints, audit supervision, etc., but they are still not fully applicable to legal forensics. A general legal forensics system must have a complete forensics process to ensure the smooth progress of the forensics work. One of the purposes of legal forensics is to use legal means to bring criminals to justice. Only by ensuring the legality of the evidence can it be accurately identified case, and therefore also to ensure the legality of the evidence collection process. In order to design a more powerful legal forensics system, this paper introduces a data mining algorithm to extract valuable information from massive data through association rules to improve the efficiency of forensics. At the same time, this paper also proposes three coding schemes for the integrity of the evidence object, and compares the forensic effects of the three schemes. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer is a leading cause of cancer-related deaths among men, marked by heterogeneous clinical and molecular characteristics. The complexity of the molecular landscape necessitates tools for identifying multigene co-alteration patterns that are associated with aggressive disease. The identification of such gene sets will allow for deeper characterization of the processes underlying prostate cancer progression and potentially lead to novel strategies for treatment. Methods: We developed ProstaMine to systematically identify co-alterations associated with aggressiveness in prostate cancer molecular subtypes defined by high-fidelity alterations in primary prostate cancer. ProstaMine integrates genomic, transcriptomic, and clinical data from five primary and one metastatic prostate cancer cohorts to prioritize co-alterations enriched in metastatic disease and associated with disease progression. Results: Integrated analysis of primary tumors defined a set of 17 prostate cancer alterations associated with aggressive characteristics. We applied ProstaMine to NKX3-1-loss and RB1-loss tumors and identified subtype-specific co-alterations associated with metastasis and biochemical relapse in these molecular subtypes. In NKX3-1-loss prostate cancer, ProstaMine identified novel subtype-specific coalterations known to regulate prostate cancer signaling pathways including MAPK, NF-kB, p53, PI3K, and Sonic hedgehog. In RB1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations involved in p53, STAT6, and MHC class I antigen presentation. Co-alterations impacting autophagy were noted in both molecular subtypes. Conclusion: ProstaMine is a method to systematically identify novel subtypespecific co-alterations associated with aggressive characteristics in prostate cancer. The results from ProstaMine provide insights into potential subtypespecific mechanisms of prostate cancer progression which can be formed into testable experimental hypotheses. ProstaMine is publicly available at: https:// bioinformatics.cuanschutz.edu/prostamine. [ABSTRACT FROM AUTHOR]

Background:Prostate cancer is a leading cause of cancer-related deaths among men, marked by heterogeneous clinical and molecular characteristics. The complexity of the molecular landscape necessitates tools for identifying multi-gene co-alteration patterns that are associated with aggressive disease. The identification of such gene sets will allow for deeper characterization of the processes underlying prostate cancer progression and potentially lead to novel strategies for treatment.Methods:We developed ProstaMine to systematically identify co-alterations associated with aggressiveness in prostate cancer molecular subtypes defined by high-fidelity alterations in primary prostate cancer. ProstaMine integrates genomic, transcriptomic, and clinical data from five primary and one metastatic prostate cancer cohorts to prioritize co-alterations enriched in metastatic disease and associated with disease progression.Results:Integrated analysis of primary tumors defined a set of 17 prostate cancer alterations associated with aggressive characteristics. We applied ProstaMine to NKX3-1-loss and RB1-loss tumors and identified subtype-specific co-alterations associated with metastasis and biochemical relapse in these molecular subtypes. In NKX3-1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations known to regulate prostate cancer signaling pathways including MAPK, NF-kB, p53, PI3K, and Sonic hedgehog. In RB1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations involved in p53, STAT6, and MHC class I antigen presentation. Co-alterations impacting autophagy were noted in both molecular subtypes.Conclusion:ProstaMine is a method to systematically identify novel subtype-specific co-alterations associated with aggressive characteristics in prostate cancer. The results from ProstaMine provide insights into potential subtype-specific mechanisms of prostate cancer progression which can be formed into testable experimental hypotheses. ProstaMine is publicly available at: https://bioinformatics.cuanschutz.edu/prostamine.

Introduction: Serum vitamin D levels are related to a wide spectrum of factors including low sunlight exposure, high oxidative stress, low physical activity and sleep disorders. In this paper we are going to investigate the most crucial parameters associated with serum vitamin D levels in survey of ultraviolet intake by nutritional approach (SUVINA) study with a data mining approach.Material and Methods: Data including demographic, anthropometric, clinical and laboratory information were extracted from the SUVINA dataset comprising 289 subjects who were enrolled into our study. The XGBoost algorithm was used to define the most important features related to vitamin D level in our population.Results: Applying XGBoost modeling for vitamin D level showed that the presented scheme can determine the most important determinants of serum vitamin D level with an accuracy of 91%. Pro-oxidant anti-oxidant balance (PAB), body fat percentage, physical activity level (PAL), age, restless leg syndrome (RLS), and dietary inflammatory index (DII) density were the most important variables correlated with vitamin D deficiency.Conclusion: Using XGBoost and with an accuracy of more than 90%, we showed that the six most important risk factors for vitamin D deficiency are PAB, PAL, age, body fat percentage, RLS and DII density, respectively.

In the fiercely competitive realm of sports and physical education, the application of data mining algorithms has emerged as a vital solution. Machine learning has streamlined processes, offering a seamless means of elevating the quality of education and training provided to students, particularly in the context of sports. This technological support empowers the sports education system to make more informed decisions pertaining to the physical development of aspiring athletes. In this comprehensive study, a blended approach of qualitative methods has been leveraged to gather intricate insights, enriching the overall understanding of the subject. Additionally, an in-depth exploration of articles and journals has been undertaken to scrutinize the practical implementation of data algorithm techniques geared towards enhancing physical training. The resultant findings underscore a substantial and tangible nexus between data algorithms and the domain of sports education. Of paramount significance is the central role played by data mining algorithms in augmenting performance. Notably, the National Sports Board (NSB) has extensively harnessed this technology to meticulously monitor players' on-field performance, ultimately leading to a granular comprehension of each player's capabilities. This paper emphasizes the methods of optimizing mistake detection and its joining systems for increasing the punishment in the operational procedures. [ABSTRACT FROM AUTHOR]

Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development of modern technology, the observation of demeanor has entered a new stage of multimodal quantification. The quantifiable results of demeanor observation with the help of scientific and technological instruments play an important role in judicial work such as search, interrogation, and evidence review. The use of man-made reasoning in the field of legal settling has become increasingly broad. Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments. The model can improve the accuracy of evidence use, and the correct use of demeanor evidence can realize the true litigation value of intelligent justice and the concept of human rights protection, and promote the construction of intelligent justice. [ABSTRACT FROM AUTHOR]

Digital media is an inevitable product of the development of information technology, and the emergence of this art has brought new opportunities and space for the innovative development of the film industry. This paper constructs a film creation model based on digital media technology and analyzes its data set using data mining algorithms. Firstly, the digital media is set as a matrix to calculate the similarity, and a subset of attributes is calculated using cosine similarity and Pearson coefficient to extract the indistinguishable relationship of rough set theory. Then the information entropy calculation scheme is reset, and the specific process of the data mining method is given. Finally, the mining is carried out for six data subsets, from which the importance of the integration of digital media and film creation is known. The results show that the sound and picture fluency tests of the frames in the film creation using digital media technology reached 8 frequencies for all three kinds of audio during the sound test. The picture smoothness of each frame of the film creation work met the design standard of >16.67ms/frame. Therefore, the combination of digital media art and film creation in modern film creation can not only promote the development of the film industry but also enhance the aesthetic ability of the audience to a certain extent.

This paper proposes the design of the Zhejiang opera culture gene map using the Zhejiang opera culture gene and gene map. Secondly, it focuses on the application of data mining algorithms in managing opera culture resources, including indexing and querying of dissemination track data and mining Zhejiang opera culture data resources. Finally, the application effect of data mining in Zhejiang opera culture gene mapping is demonstrated through an example analysis. The results show that for the multi-valued attribute dataset of trajectory data of Zhejiang opera culture, the execution time of the optimized algorithm is reduced by 848 ms under a 1% support rate and 88 ms under a 10% support rate compared with the Apriori algorithm. This paper reveals the inner connections and characteristics of Zhejiang opera culture through a data mining algorithm for cultural gene mapping, providing a scientific basis.

Introduction: Serum vitamin D levels are related to a wide spectrum of factors including low sunlight exposure, high oxidative stress, low physical activity and sleep disorders. In this paper we are going to investigate the most crucial parameters associated with serum vitamin D levels in survey of ultraviolet intake by nutritional approach (SUVINA) study with a data mining approach. Material and Methods: Data including demographic, anthropometric, clinical and laboratory information were extracted from the SUVINA dataset comprising 289 subjects who were enrolled into our study. The XGBoost algorithm was used to define the most important features related to vitamin D level in our population. Results: Applying XGBoost modeling for vitamin D level showed that the presented scheme can determine the most important determinants of serum vitamin D level with an accuracy of 91%. Pro-oxidant anti-oxidant balance (PAB), body fat percentage, physical activity level (PAL), age, restless leg syndrome (RLS), and dietary inflammatory index (DII) density were the most important variables correlated with vitamin D deficiency. Conclusion: Using XGBoost and with an accuracy of more than 90%, we showed that the six most important risk factors for vitamin D deficiency are PAB, PAL, age, body fat percentage, RLS and DII density, respectively. [ABSTRACT FROM AUTHOR]

Abstract Data stream is a type of data that continue to grow over time. For example, network security data stream will constantly be generated in the field of data security, and encrypted data stream will be generated in the privacy protection scenario. Clustering is a basic task in the analysis of data stream. In addition to the large amount of data and limited computer memory, there are the following challenges in time-decaying data stream clustering: (1) How to quickly process time-varying data stream and how to quickly save vaild data. (2) How to maintain and update clusters and track their evolution in real time. Based on the fact that the existing data stream algorithms do not provide a good strategy to the above problems, this paper proposes a dynamic clustering algorithm named SKDStream. The algorithm divides the entire data space into distinct minimal bound hypercubes, which are maintained and indexed by a newly defined structure, SKDTree, that aggregates and updates clusters in real time without requiring large primary storage. Clusters are composed of dense hypercubes. Experiments on synthetic datasets and real datasets show that the response time of the algorithm is similar to that of existing dataflow algorithms, but the quality of the generated clusters is relatively stable over time. Furthermore, the SKDStream algorithm is able to track the evolution of the number of clusters, centers, and density in real time, and compared to D-stream, SKDStream is efficient and effective in clustering.

Simple Summary: The current study was conducted to predict the live body weight using body length, heart girth, rump height and withers height of 173 Savanna goats from a stud breeder at Bysteel, Polokwane municipality, South Africa. A multivariate adaptive regression splines algorithm was used, along with the different proportions of the test and training sets to predict body weight. The body weight was best predicted from the training dataset with body weight influenced by withers height and heart girth, respectively. The interaction of withers height and body length with withers height and heart girth also influenced body weight. In conclusion, it could be suggested that the multivariate adaptive regression splines algorithm might allow Savanna goat breeders to find the best population and examine the body measurements affecting body weight as indirect selection criteria for describing the breed description of Savanna goats and aiding sustainable meat production. The Savanna goat breed is an indigenous goat breed in South Africa that is reared for meat production. Live body weight is an important tool for livestock management, selection and feeding. The use of multivariate adaptive regression splines (MARS) to predict the live body weight of Savanna goats remains poorly understood. The study was conducted to investigate the influence of linear body measurements on the body weight of Savanna goats using MARS. In total, 173 Savanna goats between the ages of two and five years were used to collect body weight (BW), body length (BL), heart girth (HG), rump height (RH) and withers height (WH). MARS was used as a data mining algorithm for data analysis. The best predictive model was achieved from the training dataset with the highest coefficient of determination and Pearson's correlation coefficient (0.959 and 0.961), respectively. BW was influenced positively when WH > 63 cm and HG >100 cm with a coefficient of 0.51 and 2.71, respectively. The interaction of WH > 63 cm and BL < 75 cm, WH < 68 cm and HG < 100 cm with a coefficient of 0.28 and 0.02 had a positive influence on Savanna goat BW, while male goats had a negative influence (−4.57). The findings of the study suggest that MARS can be used to estimate the BW in Savanna goats. This finding will be helpful to farmers in the selection of breeding stock and precision in the day-to-day activities such as feeding, marketing and veterinary services. [ABSTRACT FROM AUTHOR]

In order to improve the low accuracy of traditional wireless network health information retrieval methods, a wireless network health information retrieval method is designed based on data mining algorithm. The invalid health information stored in wireless network is filtered by data mapping, and the health information is clustered by data mining algorithm. On this basis, the high-frequency words of health information are classified to realize wireless network health information retrieval. The experimental results show that exactitude of design way is significantly higher than that of the traditional method, which can solve the problem of low accuracy of the traditional wireless network health information retrieval method. [ABSTRACT FROM AUTHOR]

Data stream is a type of data that continue to grow over time. For example, network security data stream will constantly be generated in the field of data security, and encrypted data stream will be generated in the privacy protection scenario. Clustering is a basic task in the analysis of data stream. In addition to the large amount of data and limited computer memory, there are the following challenges in time-decaying data stream clustering: (1) How to quickly process time-varying data stream and how to quickly save vaild data. (2) How to maintain and update clusters and track their evolution in real time. Based on the fact that the existing data stream algorithms do not provide a good strategy to the above problems, this paper proposes a dynamic clustering algorithm named SKDStream. The algorithm divides the entire data space into distinct minimal bound hypercubes, which are maintained and indexed by a newly defined structure, SKDTree, that aggregates and updates clusters in real time without requiring large primary storage. Clusters are composed of dense hypercubes. Experiments on synthetic datasets and real datasets show that the response time of the algorithm is similar to that of existing dataflow algorithms, but the quality of the generated clusters is relatively stable over time. Furthermore, the SKDStream algorithm is able to track the evolution of the number of clusters, centers, and density in real time, and compared to D-stream, SKDStream is efficient and effective in clustering. [ABSTRACT FROM AUTHOR]

Three‐dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high. [ABSTRACT FROM AUTHOR]

Blended teaching is a kind of teaching that combines online teaching with traditional teaching, which is defined as "online and offline". Through the organic combination of these two teaching forms, students' learning can be from shallow to deep. Therefore, based on the data mining algorithm, this paper designs the method of College English online and offline blended teaching effect. First, it collects the College English blended teaching resources, then builds the College English online and offline teaching support, debugs the College English teaching environment, and finally designs the College English blended teaching model based on the data mining algorithm, so as to realize the College English online and offline blended teaching, The experiment shows that the method designed in this paper can effectively improve the reading ability of College English, and has certain application value. [ABSTRACT FROM AUTHOR]