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Classification and Identification of Weeds Using Machine Learning Classifiers
Weeds pose significant challenges in agriculture, impacting crop yields and increasing the reliance on herbicides. Accurate and timely identification of weeds is crucial for effective weed management strategies. In this study, we propose a novel approach for automated identification of weeds using various machine learning classifiers. Our study explores the effectiveness of diverse algorithms, including Support Vector Machine (SVM), Random Forest, Decision Tree, k-Nearest Neighbors (KNN) , Extra Tree, and Gaussian Naive Bayes (NB). By pre-processing and engineering features from a diverse dataset of weed images, we ensure optimal model performance. Through rigorous experimentation and evaluation, we assess the performance of each classifier in weed identification. Notably, the Extra Tree classifier achieves an impressive accuracy of 96.35% and an outstanding kappa coefficient of 96.21%. These findings offer valuable insights into the effectiveness of different classifiers and their potential applications in precision agriculture for targeted weed management and crop optimization
Clustering Mining Method of College Students’ Physical Exercise Behavior Characteristics Based on Ant Colony Algorithm
This study adopts multi-channel data collection strategy, including questionnaire survey, intelligent bracelet data and interview observation, in order to comprehensively understand the characteristics of college students’ physical exercise behavior. Through an analysis of 2000 valid questionnaires and smart bracelet data from 500 students, we found that male college students prefer high-intensity exercise and team sports, while women prefer moderate and moderate exercise such as yoga. As the grade goes up, the proportion of high intensity exercise increases first and then decreases from freshman to senior, while the proportion of low frequency exercise increases gradually, indicating the influence of academic pressure and life rhythm changes on exercise behavior. By using ant colony algorithm to process the data, we get five clusters, each cluster has clearly defined features, so that we can identify and describe different exercise behavior patterns more accurately.
Stacking Ensemble of Capsule Network and Multi-Hidden Layer Extreme Learning Machine for Enhanced Lumpy Skin Disease Diagnosis
production and raise chances of calf mortality. This has raised the alarm on disease detection, in livestock. A novel model has been developed on the problem of skin disease, for cattle industry using stacking ensemble methods. The described approach is somewhat a transition of base models such as adjusted capsule networks and multi layered extreme learning machines which has the function of extracting important data required for the correct disease determination. Extreme Learning Machines are great for feature spaces and capsule networks are great for structures. The output will be then passed to level 2, which will use a processed random forest model as the final prediction. This complete methodology made its accuracy rate to be at 98.3 percent. When identifying and applying procedures associated with sophisticated algorithms, formers can reduce economical risks and protect herd’s health and gains to the maximum extent. This improves on sustainability and systems’ ability to cope with disease threats and enhances sustainability and resiliency in the face of disease threats.
multi-hidden layer extreme learning machine
A Novel Approach for Detection of DoS / DDoS Attack in Network Environment using Hellinger Distance Technique
As the popularity of the Internet is increasing, the number of threats is also increasing at a rapid pace. DoS attack is one such threat. DoS attacks deplete the network bandwidth, thereby, preventing the genuine users from accessing the resources. These types of attacks exhaust the computing resources of its victim without advanced warning. The goal of our research is to detect DoS/DDoS attacks effectively using the Hellinger distance technique. We have tested the performance of our approach using real network traces. Our proposed model achieved approx 91.41% Accuracy in DoS detection.We have also analyzed and compared our own dataset with standard NSLKDD dataset and UNSW_NB15 Dataset and it is observed that our proposed model has better accuracy than that of NSLKDD dataset and UNSW_NB15 Dataset which shows an accuracy of 85.29% and 81.42% respectively.
Comparative Analysis of Ensemble Learning Techniques for Brain Tumor Classification
This research explores the involved domain of ensemble learning techniques applied to brain tumor classification. With a specific focus on comparing the efficacy of homogenous ensemble classifiers, exemplified by the Random Forest (RF) algorithm, against heterogeneous ensemble classifiers like Voting and Stacking, this study embarks on a thorough evaluation journey. Our evaluation is not limited to accuracy measures only; instead, it surrounds recall, ROC AUC, precision, and F1-score, for better assessment of classifiers’ performance. Building upon an observed assessment performed on an appropriately selected brain tumor dataset, we provide solid empirical support demonstrating that RF not only performs better than base classifiers but also outperforms the heterogeneous ensemble methods in terms of many different performance measures. Furthermore, we discuss the specific reason that makes RF outperform other algorithms in this dataset and discuss the robustness and flexibility of this method. By unscrambling these insights, this paper serves to fill gaps in the existing knowledge regarding the utilization of ensemble knowledge acquisition techniques in the analysis of medical imaging especially within the area of brain tumor classification diagnostics.
Hybrid Fuzzy Metaheuristic Technique for Efficient VM Selection and Migration in Cloud Data Centers
The rapid growth in cloud computing has made it essential to maintain Quality of Service (QoS) across varying workloads. Virtual machine (VM) migration plays a pivotal role in enhancing service efficiency by effectively managing resources. Yet, frequent VM migrations can lead to higher energy use and reduced performance. To tackle these issues, a novel Fuzzy-based Hybrid Optimization Algorithm, FCSFFC, which combines Fuzzy Cuckoo Search with Fuzzy Firefly Colony Optimization has been developed. The proposed method strives to reduce power usage, resource waste, computation time, and migration expenses. Simulations validate our method's superiority over existing techniques in meeting our goals. Fuzziness in the algorithm helps manage the uncertainty and vagueness in predicting workloads and resource allocation, thus making the system more flexible and robust. With fuzzy logic, our model dynamically fine-tunes migration plans according to live conditions, improving the precision of decisions. The FCSFFC model, with its dynamic threshold-assisted load prediction, has been shown to minimize performance decline, downtime, and the number of VM migrations.
Enhancing Data Integrity in Computerized Accounting Information Systems Using Supervised and Unsupervised Machine Learning Algorithms Implement A SEM-PLS Analysis
The paper determines Machine Learning (ML) applications of both supervised and unsupervised types in computerised accounting information systems (CAIS) to improve data consistency. The Partial Least Squares Structural Equation Modelling (SEM-PLS) approach was used for the processing of data, which comprised 163 building companies in China that were using Building Information Modelling (BIM). This paper examines the financial data in view of ML algorithms and looks into the way ML improves financial data accuracy, consistency, reliability, and consistency. The results revealed that the integration of ML algorithms could increase data integrity (DI) by as much as 27% and detection of error by as much as 35% compared with manual methods. These results emphasise artificial intelligence (AI) solutions' leading role in improving CAIS-focused financial decision-making and operational control systems, demonstrating how AI can be applied to the field efficiently. By investigating the impact of ML on data integrity, which is measured through advanced SEM-PLS technique, this research is among the first studies on AI finance to deepen the knowledge.
Hybrid Fuzzy Data Aggregation and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks
Wireless sensor networks (WSNs) are networks with many sensor nodes that are utilized for various purposes, including the military and medical. In hazardous circumstances, precise data aggregation and routing are essential, and the energy consumption of the sensors needs to be closely controlled. Nonetheless, there is a significant chance of redundant data because of external factors and nearby sensors. A multitude of information can be found in large datasets, some of it unnecessary and others useful. This redundancy negatively impacts performance in terms of redundant transmission and computing costs. However, data aggregation might help a network get rid of unnecessary data. In this work, we present a hybrid protocol called fuzzy data aggregation with fuzzy spider monkey optimization routing protocol (FDA-FSMORP) that represents an intelligent approach to collecting sensor data in HWSNs considering energy consumption. The results indicated that the suggested method beat in minimizing data latency our approach reduced energy consumption by 73% using energy more effectively when compared to our simulated outcomes
Optimization of Prediction Intensity of Big Data Clustering Algorithm Integrated with Distributed Computing in Cloud Environment
Through the existing hardware resources such as servers and storage in the data center, the use of virtualization technology to integrate hardware resources and network resources, etc., build a data center private cloud management platform, which can optimize the highly standardized servers and fully improve the hardware Resource utilization. Through a unified private cloud management platform, the rapid deployment of hardware resources can be achieved and the operability of data can be enhanced. It can meet most analysis tasks, quickly respond to analysis needs and improve the efficiency and quality of system management. Realize the rational allocation of data resources with minimal management cost and workload. During the period of continuous adjustment of the business system, the school can use the cloud data center to allocate resources according to the needs, meet the real-time deployment requirements of IT and carry out real-time risk monitoring of cloud data, identify threat programs for isolation processing and feedback and perform daily business operations Abnormalities are collected and hidden dangers are discovered and early warning is given in time. On the other hand, service providers can also develop customized security services based on the level of corporate information security requirements.
Gender Classification on Twitter Based on Feeds and User Descriptions Using Xlnet-Fasttext
Gender falsification in social media content is an increasingly troubling challenge, with users often choosing to hide their true gender identity or pretend to be members of a different gender. This can lead to negative consequences, including the spread of disinformation, discrimination and online security risks. To overcome this problem, this research proposes a text classification-based solution to identify gender fakes in social media texts. This method involves extracting linguistic features from texts, such as word usage, sentence structure, and language patterns that can provide clues to the author's gender. Therefore, this research aims to introduce a new transformers-based approach that uses XLNet and is also modified with additional Fasttext embedding. Modifications were made to the embedding section which can increase XLNet's understanding of text context in carrying out text classification. The results of this research are that baseline XLNet gets a fairly good performance score in gender classification based on Twitter feeds, namely with accuracy, precision, recall and f1-score of 0.704, 0.770, 0.598, 0.674 respectively, while XLNet-FastText gets the respective scores. -respectively 0.714, 0.770, 0.609, 0.680. And for gender classification based on user account descriptions, baseline XLNet gets scores of accuracy, precision, recall, f1-score of 0.705, 0.771, 0.598, 0.674 respectively while XLNet-FastText gets scores of 0.724, 0.751, 0.6324, 0.686 respectively.
Hybrid GA-ACO algorithm for optimizing transportation path of port container cargo
With the advancement of port transportation, optimizing the transportation path of container cargo has become a crucial consideration for logistics transportation companies. In this paper, a path optimization model was established for transporting container cargo from the yard to the customer, considering the customer's time window and aiming to minimize the total cost. A genetic algorithm-ant colony optimization (GA-ACO) algorithm was then devised to solve the model, and a case was analyzed to verify the effectiveness of this approach. It was found that the total cost of the path obtained by the GA-ACO algorithm was significantly lower than that of the GA and ACO individually (8.63% and 12.96%), reaching 7,458,268 yuan. Moreover, it used fewer vehicles. It suggested that the GA-ACO algorithm yielded a more efficient result. An analysis of different task quantities revealed that as the number of tasks increased, logistics transportation enterprises achieved higher vehicle utilization rates and better economic efficiency in completing container cargo transportation. These findings validate the reliability of the GA-ACO algorithm, affirming its applicability in real-world optimization of port container cargo transportation paths.
Novel AI Model for Evaluating Buyers' Fulfilment with Clothing Fit
Online merchants must make sure that their customers are satisfied with the fit of their products, yet standard size charts sometimes fall short of accounting for the wide variety of body types and preferences. This research presents a novel artificial intelligence (AI) model that aims to precisely assess buyers' fulfillment with clothing fit (BF-CF). As test clothes, skirts composed of various textiles were utilized. The BF-CF was estimated by assigning predictor factors to the mechanical characteristics of the skirt textiles. Using a 3D body scanner, virtual body models of the study participants were produced and utilized for virtual fitting. Each participant evaluated how well the clothes fit after trying them on in person. Additionally, participants evaluated the virtual fit, which indicated how well they fulfilled the virtual fit (PVFF), by looking at the skirt simulations on their avatars. The data was warmed up to predict BF-CF using a novel machine learning technique called Tabu search optimized smooth linear logistic regression (TSO-SLLR). According to the experimental data, PVFF was the most important factor. In the fashion business, machine learning (ML) is mostly utilized for manufacturing and sales forecasting. Nevertheless, studies on the topic of clothing fit, which deters people from buying online, received little attention. Thus, we proposed an innovative machine learning approach in this work to forecast customers' fulfilment with clothing fit. This study shows how the AI model works to increase customer happiness, decrease returns, and improve the entire online shopping experience for both customers and clothes merchants through extensive testing and validation.
Log Data Mining of User Purchase Behavior Based on Distributed Intelligent Optimization Algorithm
As the development of e-commerce becomes more and more intelligent, higher requirements have been put forward for the algorithms controlling e-commerce operations. However, the current e-commerce operation is not timely and accurate enough to update the purchase data and statistics, resulting in cost consumption and revenue is not proportional, and can not accurately meet the user favorite. To speed up the collection of user purchase behavior data and improve the revenue of e-commerce operations, the study introduces adaptive degree values based on a distributed computing framework combined with a topological structure. The computing framework is used to speed up the calculation and convergence of user data, and the topology is responsible for classifying the data in the dataset and calculating the optimal location. In the classification accuracy experiment, the accuracy of the improved algorithm was above 94% and up to 98%. In the stability experiment, compared with other algorithms, the stability of the improved algorithm was improved by 81.2%. In the simulation experiment, the overlap between the noise value of beauty search 2000-2700 and the noise value of clothing matching 2000-2500 in the shopping platform was large. Therefore, there was a correlation between the user's search for clothing collocation and the beauty search. In summary, the performance of the improved algorithm is superior in terms of stability, accuracy and application error. Therefore, the study of the improved algorithm has a better application for data mining of user purchase behavior.
A Deep Transfer Learning Framework for Robust IoT Attack Detection: A Review
Our lives have been significantly altered due to the digital revolution, and the Internet of Things (IoT) has played a significant part in this transformation. However, the fast expansion of the IoT into almost every aspect of life has resulted in various new cybersecurity dangers. As a result, detecting and preventing possible attacks on IoT networks have lately garnered significant attention from the academic and business worlds. Machine learning (ML)-based techniques, particularly deep learning (DL), have shown significant promise among the many different approaches to attack detection. This is because they can identify attacks at an early stage. However, for these DL algorithms to be effective, gathering substantial data from IoT devices, including label information, is necessary. On the other hand, the labelling process is often resource-intensive and time-consuming; hence, it may not be able to accommodate rapidly growing IoT threats in the real world. The introduction of DL methods to the IoT datasets is the main emphasis of this study, which also reviews the newest advancements in security measures for threat detection. This review aims to examine DL techniques and continuing breakthroughs in approaches that may be used to produce enhanced attack detection models for IoT frameworks. This is the objective of this review. When applying DL to IoT security, we address the benefits and research gaps associated with each strategy.
Tourism Destination Recommendation based on bag of Visual Word Combined with SVM Classification
In recent years, people's living standards have gradually improved, and more and more people plan to travel. In response to the low accuracy of user travel destination recommendation, this study proposes a travel destination recommendation method combining bag of visual word with support vector machine classification. Firstly, the study introduces a convolutional neural network extractor to improve bag of visual word. Subsequently, domain adaptation is introduced to address the distribution differences between the target feature data and the source domain feature data. Finally, by improving the support vector machine, the scenic spots that users are interested in are classified. Similarity calculation is adopted to achieve tourism destination recommendation. In the results, the accuracy of the collaborative filtering algorithm started to increase from 34.5%, and then reached 36.6% when the nearest neighbor users were 50 and tended to stabilize. The recall of collaborative filtering algorithm was 42.1% when the nearest neighbor users were 20, and 43.6% when the nearest neighbor users were 50. The proposed recommendation algorithm’s accuracy was 42.6% when the nearest neighbor users were 20, and 44.9% when the nearest neighbor users were 50. The recall started to increase from 43.8% and stabilized at around 44.8% when the nearest neighbor users reached 50. Overall, the designed tourism destination recommendation algorithm has strong practical applicability. This algorithm provides a strong recommendation strategy for many users to travel and helps them efficiently choose their desired attractions.
A Deep Intelligent Ant Colony-Based Approach to Personalized and Customized Route Optimization for Smart Tourism
To enhance the travel experience of tourists and make the shortest travel personalized route, an innovative personalized route optimization method based on a deep, intelligent ant colony is proposed. The algorithm considers the tourists' travel time, cost constraints, and the experience of attractions. It establishes the objective function of tourists' personalized route optimization to maximize the utility of their travel and tourism activities and minimize the total path. Based on the attention mechanism neural network, the objective function feature matrix is extracted, which is used to replace the heuristic information matrix of the ant colony algorithm, and at the same time, the guidance information of the target point is added to understand the optimization of the ant colony algorithm. The optimized algorithm solves the objective function and obtains the optimization results of the personalized, customized route for intelligent tourism. The test results show that the method can calculate the feature matrix between different attractions, and the time reduction rate is between 44.6% and 68.2%; it can complete the optimization of personalized routes under other circumstances; it can obtain the shortest route planning results under the condition of guaranteeing the maximum utility of tourists, and the actual multi-objective shortest path proximity and single-objective average degree of realization are above 93.36%.
An Empirical Study on Enhancing Tourism Service Quality in 5G Multimedia Environments with Deep Learning
The purpose of this study is to improve the quality of tourism service through deep learning technology, and to conduct an empirical study. By comprehensively analyzing the challenges and key factors of tourism service quality as well as the application status of deep learning in the field of tourism, an optimization strategy of tourism service quality based on deep learning is designed, and experiments and results are analyzed. The research results show that the strategy has certain feasibility and potential effect, which can accurately analyze user comments and demands, and provide targeted suggestions for improvement. However, there are still some challenges in terms of data quality and model selection. This study provides a strategy based on deep learning for tourism service providers and researchers, and has certain guiding significance for improving the quality of tourism service.
Enhancing Colonoscopy Image Quality Through Multi-Step Computational Pre-Processing Techniques
Colonoscopy is a crucial procedure for gastrointestinal diagnostics, providing direct visualization of the colon's internal structure. The quality of acquired colonoscopy images significantly impacts diagnostic accuracy and treatment planning. This study focuses on enhancing colonoscopy image quality through computational multi step image processing techniques aimed at enhancing colonoscopy image quality and interpretability. The methodology involves multi step strategy for evaluating various noise reduction filters including Gaussian, bilateral, and hybrid bilateral-Gaussian filters, along with Contrast Limited Adaptive Histogram Equalization (CLAHE) and Unsharp Masking techniques. Evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to quantify the efficacy of these techniques. The study employs the CVC Clinic DB dataset for experimentation, ensuring clinical relevance and diversity in the images analyzed. Results from ablation studies and quantitative analyses highlight the effectiveness of specific preprocessing techniques in preserving image details, enhancing contrast, and sharpening edges. In first step, the hybrid bilateral-Gaussian filter achieved as a suitable noise reduction filter, followed by CLAHE and edge enhancement using Unsharp masking. The PSNR and SSIM values from the first to third step shows increase of 2.88% (from 37.86 dB to 38.95 dB) and 1.56% (from 0.96 to 0.975) respectively. The study's findings contribute to advancing gastrointestinal diagnostics, aiding in more accurate diagnoses, treatment planning, and patient outcomes
ENVI-met Simulation of Green Plant Layouts for Urban Thermal Comfort Optimization
Different green plant layouts exert crucial impacts on human thermal comfort. The existing green plant layouts in urban streets have not achieved more ideal results in regulating temperature, humidity, and solar radiation. Therefore, in order to design a more livable urban environment, this study analyzes the green plant layout pattern of Street A in a certain city and explores its impact on regional thermal comfort. The study first designs different green plant layouts. Then, the meteorological factors that contribute to the thermal comfort of the region based on an urban microclimate treatment model are analyzed. Finally, the thermal comfort is evaluated using the predicted mean vote-predicted percentage of dissatisfied. The experimental results showed that the average daily predicted mean vote values of the four different simulation schemes were 0.55, 0.71, 0.46, and 0.21, respectively, indicating that shrubs and arbors had a significant regulatory effect on human thermal comfort. There were obvious differences in the improvement effect of different green vegetation arrangements on thermal comfort. The overall improvement effect first increased and then decreased. The designed green vegetation arrangement can more effectively regulate regional microclimate, improve local thermal comfort, and provide a more comfortable and livable working and living environment.
Application of Fuzzy Decision Theory in Multi Objective Logistics Distribution Center Site Selection
In the context of globalization and the rapid development of e-commerce, the location decision of logistics and distribution centers has become more and more critical, especially in the emerging markets, where enterprises need to take into account social responsibility and environmental sustainability while pursuing economic benefits. This study focuses on solving the multi-objective optimization problem of logistics and distribution center site selection, using fuzzy set theory and multi-objective decision analysis, combined with particle swarm optimization algorithm (PSO), an innovative fuzzy multi-objective particle swarm optimization algorithm (FMOPSO) is proposed. Through a case study of a retail company in Asia, we validate the effectiveness of FMOPSO in dealing with ambiguity and uncertainty in site selection decisions. The research process includes exhaustive data collection and preprocessing, construction of a multi-objective fuzzy decision model, and application of the FMOPSO algorithm. The results show that the A-02 address has the best performance in terms of cost-effectiveness, environmental impact, and customer service quality, and the FMOPSO algorithm demonstrates superior performance in solving this kind of complex problems, which provides a scientific basis and practical guidance for decision makers.
Low-cost Badminton Trajectory Recognition and Landing Point Prediction Optimization Based on Field Coordinate System Transformation
Judging the flight trajectory curve and predicting the landing point are important factors affecting the quality of hitting in badminton matches. Many badminton professionals and coaches usually analyze the strength of badminton matches through video analysis to improve their own abilities and develop competition strategies. This study proposes a field coordinate transformation strategy for badminton trajectory recognition to reconsider the badminton field, and then proposes a badminton detection tracking and trajectory prediction algorithm based on video data streams. This algorithm is divided into badminton tracking, recognition detection, and trajectory prediction. The results indicated that outdoor environments had better accuracy in anchor coordinate conversion. The offset distance for anchor coordinate conversion was above 0.75m indoors and between 0.25m-0.05m outdoors. The recognition accuracy was over 95%, with a minimum of around 80%, and the algorithm took between 5s to run in different environments. During different stages of operation, the average running time, average coordinate calculation time, and average trajectory prediction time of the algorithm were 621.25ms, 16.75ms, and 342.875ms, with a standard deviation of 12.277, 0.782, and 4.552. The research algorithm was cost-effective in terms of running time and effectiveness. The improved algorithm in this study had good recognition performance with accuracy, recall, F1, and frame rate of 98.6%, 97.6%, 98.7%, and 30.8 frames/s, respectively. The research methods and results have promoting significance for the development of badminton sports.
Visibility Detection Technology in Highway Engineering Considering Image Processing Technology Combined with Deep Learning Algorithms
In traditional highway engineering, there are problems such as high cost and difficult maintenance in road visibility detection. Therefore, the study combines Hough circle detection algorithm with incremental probability neural network to construct a visibility detection model for highway pavement. The study first uses the Hough circle detection algorithm to perform preliminary visibility detection, and then integrates the incremental probability neural network with the preliminary detection to construct a detection model. These results confirm that the data processing accuracy and precision of the detection model in the image processing process are 97.03% and 93.37%, respectively. In terms of feature classification performance, its classification ability and classification time are 93.61% and 1.13 seconds, respectively. And, its visibility and error percentage in visibility detection are 510.69 m and 10.06%, respectively. These indicate that the highway pavement visibility detection model can improve the accuracy and stability of highway pavement visibility detection. The research aims to provide an effective visibility detection method for highway traffic safety.
Enhanced Krill Herd Algorithm Using Shuffled Frog Leaping and Meme Grouping for Multi-Objective Optimization Problems
To solve the low performance problem of krill herd algorithm in the face of multi-modal optimization problems, this study proposes an improved krill herd algorithm based on a hybrid frog leaping algorithm and meme grouping method. This study analyzes the global optimization and local distribution behavior characteristics of the krill herd algorithm. Then, combined with the hybrid frog leaping algorithm, the krill individuals are optimized through meme grouping to enhance the algorithm's global and local search capabilities. This study conducted MATLAB simulation experiments to test the Schaffer function and Griebank function, and compared the results with traditional krill herd algorithms. The results showed that the improved algorithm began to converge in the search of Schaffer and Griebank functions at the 32nd and 68th iterations, and basically completed convergence at the 64th and 130th iterations, with minimum errors of 3% and 5%, respectively. The minimum errors of traditional krill herd algorithms were 5% and 8%. This study further validated the algorithm through logistics scheduling and showed that the optimized algorithm shortened the completion time of scheduling tasks by 3 hours and reduced costs by 13,500 yuan. Research has shown that the proposed method performs outstandingly in improving global optimization capability and computational efficiency, and has practical application value.
Data Mining Technology for Privacy Protection in Distributed Scenarios
In the Internet era, data mining is an important means to seize users. However, data exists on different platforms, which are incompatible with each other, and user privacy is easily leaked when mining data. To address this issue, a distributed data mining method based on differential privacy is proposed. The method aggregates frequent itemset data from the top m items of branch nodes through a central node. The decision tree algorithm is used as a data classification method to set privacy budgets, optimize count queries, and perform importance attribute filtering. The experimental results showed that the improved algorithm had an average increase of 0.1 in data mining accuracy, an average increase of 0.115 in relative error, and an average decrease of 0.08% in privacy leakage probability. The data classification accuracy of the improved algorithm increased by an average of 0.28, and the privacy leakage probability during data classification decreased by an average of 0.06%. From this, the improved algorithm can significantly improve the accuracy of data mining and classification, significantly reduce the privacy budget required for data mining and classification, reduce the probability of privacy leakage, and greatly improve the security of user data.
Detection Model of Water Logged Area in Goaf Based on Multi-source Data Fusion and Group Intelligence Perception Computing
By the perception computing, for improving the efficiency of mine mining and improve its security, and multi-source data fusion and swarm intelligence sensing computing technology have the advantages of fast response rate, high sensitivity and strong applicability, this paper combines multi-source data fusion and group intelligence perception computing technology, through pre detection and analysis of the structure of the mine stratum, discusses the function principle and attention process of two Internet technologies in detail, and establishes a detection model of water logged area in the mine goaf by the group intelligence perception computing. Then, this model is applied to the mine mining between different cities. By comparing the application rate and satisfaction of this detection model between different cities, the corresponding measures to improve the application rate of this detection model are proposed. In a word, this paper combines theory with practice, takes the goaf ponding area as the main target, establishes the corresponding model, and successfully applies it. This exploration model is of great significance to improve the development efficiency of the mine and perception computing.
Dynamic Anomaly Detection in Resource-Constrained Environments: Harnessing Robust Random Cut Forests for Resilient Cybernetic Defense
Investigating non-parametric anomalies and analyzing the influence of external factors on data integrity, uncovering hidden patterns amid dynamic fluctuations. This paper ex amines anomaly detection in resource-constrained environments using robust random cut forests. Begin with a detailed exploration of resilient random cut data structures for ana lyzing incoming data streams in Internet of Things environment. The methodology used in this paper is to evaluate using diverse datasets, including real-time Arduino data and publicly available sources, to assess algorithmic performance across different scenarios. This research contributes to the theoretical foundations of anomaly detection, stressing the need for adaptive approaches in evolving data landscapes. By employing robust random cut forests, the challenges posed by continuous data streams are addressed, en suring accurate identification of trends and anomalies over time. In summary, this study provides a comprehensive examination of anomaly detection in dynamic data streams, presenting a novel methodology grounded in resilient random partitioning forests. The integration of theoretical insights, updating strategies, and empirical experimentation lays the groundwork for future advancements in dynamic anomaly detection under re source constraints.
Optimization Method of Basketball Match Evaluation Based on Computer Vision and Image Processing
The accuracy and stability of real-time target detection in computer vision during basketball games has always been a challenge. In light of this, the study examines the shortcomings of computer vision systems for target tracking and target detection first. On this basis, it also introduces the faster region convolutional neural network algorithm for target detection optimization. Concurrently, to further enhance the model's target tracking proficiency, the study incorporates the enhanced pyramid optical flow algorithm and refines it through the application of Kalman filtering. Ultimately, a pioneering target detection and tracking model is proposed, integrating the optical flow algorithm and the faster region convolutional neural network. The experimental results indicated that compared with the same type of target detection and tracking models, the novel model proposed by the research had relatively excellent target tracking and localization effects. The error between its true trajectory and tracking trajectory was less than 3%. Moreover, the overall performance of the research-proposed model had the highest score in the three index tests of target position precision, accuracy and recall mean, and target tracking accuracy. The highest value of target position precision reached 93.57%, the highest value of accuracy and recall mean reached 95.02%, and the highest value of target tracking accuracy reached 91.57%. In conclusion, the novel target detection and tracking model proposed in the study demonstrates the capacity to markedly enhance the detection and recognition performance of the existing target detection and tracking model. Furthermore, it offers substantial support for the advancement of optimization methods for evaluating basketball games.
Cloud Computing Resource Scheduling Method Based on Optimization Theory
To propose a reliable cloud computing task deployment algorithm for the optimization theory. The current research on cloud computing task deployment mostly only focuses on one of the two goals: reliability and optimization theory. This paper studies how to provide fault tolerance for task execution failure while minimizing the number of servers used to perform all tasks, thus reducing the problem of optimization theory. This article provides fault recovery capability through task replication, providing two instances of each task that makes up the job. Task copies can be deployed either on a dedicated backup server or to the server where the main task is located, by sharing the same computing resources and running at less than the execution speed of the main task. We propose a reliable cloud computing task deployment algorithm for optimizing theoretical optimization and service quality perception. For users, the completion time of the service is usually limited, and if a timeout occurs, it will cause a loss to the cloud service provider. For the actual completion time performance of the task at the last moment, the algorithm RER is about 2% to 10% more than the algorithm QSRE at xtr = 0.75. Time out times of the algorithm RER (xtr = 0.75). If the task fails at a random time, the algorithm RER (xtr = 0.75) has 10% -15% probability over the execution time period of the job, and the algorithm RER has 42% to 63% probability of timeout. The algorithm RER (xtr = 0.5) is 12% to 22% less than the algorithm QSRE. This paper studies how to minimize the number of servers used to perform all task copies while ensuring service quality and providing fault tolerance, thus reducing the problem of optimization theory. This deployment for different sizes of task copy of different sizes, deployed to a dedicated backup server with different execution speed, to ensure that the fault after the task copy can still be completed in the user required time, this paper proposed a service quality perception and energy saving reliable task copy deployment algorithm. This method can control the prediction error below 2%. For the historical data of resource usage with non-linear input data amount, sample prediction value and error prediction value. This strategy weights different sample data and takes the error prediction value as the correction parameter of the final prediction value. Compared with the original least squares method and Hyper Log base estimation algorithm, using the optimization strategy in this paper can reduce the prediction error by about 70% and 50%, respectively. For the task scheduling problem of cloud computing, this paper proposes a priority allocation algorithm TDP based on task latency time. Task latency time refers to the longest time in which the task can be delayed under the premise of not affecting the completion time of the task.
Construction of Human Resource Management System Based on Machine Learning and Analysis of Influencing Factors
In this study, we present an in-depth analysis of the development of human resource management systems that leverage machine learning techniques, as well as the factors that influence their construction. Drawing upon extensive data analysis, our findings illuminate the constructive impact of these systems on enhancing organizational performance. To provide a nuanced understanding, we compare and contrast traditional human resource management practices with contemporary systems that integrate machine learning in recruitment, training, and performance evaluation processes. The results of data analysis show that the system based on machine learning shows higher efficiency and accuracy in many aspects. Specifically, in the recruitment process, the method based on machine learning successfully predicted the matching degree of candidates, and improved the recruitment success rate to 45%, compared with the traditional method, the success rate was 30%; In the training session, the machine learning algorithm makes personalized recommendations according to the learning situation and learning needs of employees, which improves the training effect by 35%, and the improvement rate is 20% compared with the traditional method; In performance evaluation, the objective evaluation method based on machine learning reduces the interference of human factors, improves the fairness and accuracy of evaluation, and the accuracy rate is increased by 50% compared with traditional methods.
A Study of Starting Correction in Sports Based in Human Posture Estimation and Attention Mechanism
The standardisation and accuracy of the starting movement will affect the athletes' performance and the results of the game, and the differences in different sports, athletes' physical qualities and coaches' teaching methods make it difficult for the traditional correction of the starting movement to reflect a better application effect. Therefore, the study is based on the Openpose human posture estimation and recognition algorithm, which performs frame-posture relationship correlation analysis, continuity movement design and joint bone point repair to improve the accuracy of recognition. At the same time, taking into account the differences in the content of different assignments of posture data information, the study introduces a lightweight attention mechanism that focuses on temporal patterns on the basis of the recognition algorithm. The experimental results show that the recognition accuracy of the improved algorithm for the starting posture is above 0.85, and its recognition accuracy for the three phases of the starting action is above 93% compared with the Dynamic Time Warping (DTW) model and the High Resolution Human Keypoint Detection Network (HRNet-w48), with an F1 value of 0.6857, which is better than that of the DTW model (0.6857), and the F1 value of 0.6857. better than the DTW model (0.6214) and HRNet-w48 model (0.6321). The detection and recognition time of the improved algorithm for the starting movement is less than 0.1s, and it can better provide athletes with movement correction suggestions and formulate personalised training plans.