In an era dominated by big data, this study focuses on creating precise student profiles and developing personalized recommendation systems, leveraging advancements in big data technologies and algorithms. It delves i...
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ISBN:
(纸本)9798400709784
In an era dominated by big data, this study focuses on creating precise student profiles and developing personalized recommendation systems, leveraging advancements in big data technologies and algorithms. It delves into the significance of student profiling and personalized recommendations, highlighting the constraints of traditional kmeans algorithms in this context. The research thoroughly explores big data analysis concepts, detailing the kmeans algorithm's principles, limitations, and the theoretical basis for its enhancement. Central to the study is the role of big data in building student profiles, encompassing data collection, cleansing, and feature extraction processes. Utilizing an improved kmeans algorithm, the study achieves enhanced accuracy and detail in student profiling. Additionally, it designs a personalized recommendation system by selecting suitable algorithms that align with student profiles and proposes metrics to evaluate their effectiveness. The experiments conducted affirm the proposed methods and system's efficacy. This research contributes to the field by applying big data and algorithmic improvements to refine student profiles and recommendation systems, while acknowledging existing challenges in data processing and algorithm scalability, and suggesting directions for future optimization.
Timely detection of network intrusion is an indispensable part of network security;therefore, how to accurately detect network intrusion is particularly important. Based on this, this paper proposes an intrusion detec...
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ISBN:
(数字)9789811924484
ISBN:
(纸本)9789811924484;9789811924477
Timely detection of network intrusion is an indispensable part of network security;therefore, how to accurately detect network intrusion is particularly important. Based on this, this paper proposes an intrusion detection model based on Apriori-kmeans algorithm to detect network intrusion, in order to ensure the normal operation of the network. Firstly, the intrusion alarm mode under the security state of feature-based NIDS is modeled, and the continuous output of NIDS intrusion alarm is filtered by Apriori-kmeans algorithm, then the similarity between points is determined by the modified distance D in Apriori-kmeans algorithm, and whether the new data point is normal or not is judged according to the similarity score, so as to reduce the false alarm rate of intrusion detection system, improve the accuracy of intrusion detection. A comparative experiment is designed, and the results show that the method proposed in this paper has high accuracy.
We propose a feature extraction method based on the kmeans algorithm based on the text characteristics in the English translation corpus. The article first uses a sparse autoencoder unsupervised learning method to red...
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We propose a feature extraction method based on the kmeans algorithm based on the text characteristics in the English translation corpus. The article first uses a sparse autoencoder unsupervised learning method to reduce dimensionality. It then uses the kmeans clustering algorithm for text clustering. The experimental results prove that the text features extracted by the sparse autoencoder based on the kmeans algorithm can be used for English translation corpus knowledge clustering to achieve automatic integration. And this method can effectively solve the problems of high-dimensional, sparse, and noisy texts in the English translation corpus. The algorithm mentioned in the article can significantly improve the accuracy of the clustering results.
In order to extract complete leaf image contours of cowpea diseases under natural environment, cowpea disease leaf image segmentation method combining squirrel search algorithm and kmeans clustering algorithm was prop...
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ISBN:
(纸本)9781450397148
In order to extract complete leaf image contours of cowpea diseases under natural environment, cowpea disease leaf image segmentation method combining squirrel search algorithm and kmeans clustering algorithm was proposed. Firstly, the images were converted from RGB color space to HSV color space; then the squirrel search algorithm was used to obtain the initial cluster centers to improve the kmeans algorithm for image segmentation; morphological operations were used to smooth the images; finally, after removing small area noise, the Canny algorithm was used to extract the complete cowpea diseased leaf outline. The experimental results show that the cowpea diseased leaf images segmented by the algorithm used in this paper have smooth edges and can effectively segment cowpea leaves, which provides a basis for the application of computer vision in cowpea disease identification.
Cloud communication is a combination of distributed computing and parallel computing. One of the biggest challenges in cloud communications is task scheduling, which is difficult due to the nondeterministic polynomial...
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Cloud communication is a combination of distributed computing and parallel computing. One of the biggest challenges in cloud communications is task scheduling, which is difficult due to the nondeterministic polynomial completeness (NP) of cloud systems. To solve this problem, various approximation techniques based on swarm intelligence have been developed. This study proposes a dual machine learning strategy using kmeans to optimize performance and aid in selecting cloud scheduling technologies. The first technique is called Efficient kmeans (Ekmeans) and the second technique is called kmeans HEFT (KmeanH), where HEFT stands for Heterogeneous Earliest End Time. Our main contribution is to reduce processing time and increase speed and efficiency for a given set of tasks. We evaluate the impact of both algorithms on different virtual machines (ranging from 2 to 32) and task sizes (ranging from 50 to 1000).
Hydrogen energy is significant in the energy consumption, especially in Hydrogen Fuel Cell Vehicles(HFCVs) market. Social media data is critical for exploring public perceptions of HFCVs. To find hot topics and unders...
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Hydrogen energy is significant in the energy consumption, especially in Hydrogen Fuel Cell Vehicles(HFCVs) market. Social media data is critical for exploring public perceptions of HFCVs. To find hot topics and understand the public sentiment of HFCVs, we employ a computational model, which combines kmeans algorithm, Latent Dirichlet Allocation (LDA), and SnowNLP. The training data consists of 42,063 comments sourced from Bilibili-a popular Chinese social media platform. The analysis has identified 12 clusters, each with distinct topics and sentiments. The results reveal that the Chinese public generally holds a neutral stance on the hydrogen energy market, while some stakeholders maintain a positive on the technology and development of HFCVs, but some concerns about the transportation and safety of hydrogen fuel. Furthermore, this study offers suggestions for the technological, operational, and strategic advancement of HFCVs.
作者：
Xu, LiKong, MingmingPan, BingSouthwest Jiaotong Univ
Postdoctoral Stn Chengdu 610039 Sichuan Peoples R China Xihua Univ
Collaborat Innovat Ctr Sichuan Automot Key Parts Postdoctoral Stn Chengdu 610039 Sichuan Peoples R China Xihua Univ
Ctr Radio Adm Technol Dev Chengdu 610039 Sichuan Peoples R China
This paper proposes a novel building extraction method from satellite imagery. Intuitively, the symmetry and regularity of architecture could be used to detect as building area, which benefited from the stroke width t...
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ISBN:
(纸本)9789811073052;9789811073045
This paper proposes a novel building extraction method from satellite imagery. Intuitively, the symmetry and regularity of architecture could be used to detect as building area, which benefited from the stroke width transform (SWT) algorithm. Meanwhile, the roof of building are very different with rural area (such as vegetation, wild, etc.) in color space, which can be partitioned by K-means clustering method. The area clustering can obtain the consistency region to complement the discontinuity from SWT algorithm detection. Then, A superpixel generation algorithm is adapted to yield the color distribution of building region, and final building area is able to extract accurately. Different with existing methods, the proposed method performs on a single source satellite image without any other supplement information. Experiment on a large number of satellite imagery demonstrates the efficiency of the proposed method for building extraction.
Quantum dots-labeled urea-enzyme antibody-based rapid immunochromatographic test strips have been developed as quantitative fluorescence point-of-care tests (POCTs) to detect helicobacter pylori. Presented in this stu...
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Quantum dots-labeled urea-enzyme antibody-based rapid immunochromatographic test strips have been developed as quantitative fluorescence point-of-care tests (POCTs) to detect helicobacter pylori. Presented in this study is a new test strip reader designed to run on tablet personal computers (PCs), which is portable for outdoor detection even without an alternating current (AC) power supply. A Wi-Fi module was integrated into the reader to improve its portability. Patient information was loaded by a barcode scanner, and an application designed to run on tablet PCs was developed to handle the acquired images. A vision algorithm called kmeans was used for picture processing. Different concentrations of various human blood samples were tested to evaluate the stability and accuracy of the fabricated device. Results demonstrate that the reader can provide an easy, rapid, simultaneous, quantitative detection for helicobacter pylori. The proposed test strip reader has a lighter weight than existing detection readers, and it can run for long durations without an AC power supply, thus verifying that it possesses advantages for outdoor detection. Given its fast detection speed and high accuracy, the proposed reader combined with quantum dots-labeled test strips is suitable for POCTs and owns great potential in applications such as screening patients with infection of helicobacter pylori, etc. in near future.
Data mining is defined as extracting and analyzing information from various heterogeneous data sources to generate the user interested patterns. In this paper, data mining is used for such a purpose where in which it ...
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ISBN:
(纸本)9781509006120
Data mining is defined as extracting and analyzing information from various heterogeneous data sources to generate the user interested patterns. In this paper, data mining is used for such a purpose where in which it helps in increasing the marketing of a respective educational organization. Students will come from different localities to join a prospective college. In the proposed system, instead of places, the distance from the address of the student residence destination is analyzed. It provides a more accurate idea about the upcoming year marketing. Distance can be calculated by using the Haversines formula and the clustering algorithm k-means can be used to cluster the locations to get more accurate results. Google maps API is used to find out the latitude and longitude of each student residential address and visualized, which gives the minimum, maximum and average distance. The pictorial representation helps the organizations to concentrate more on specific areas where the better advertisement can be given to improving the admission rate.
In this paper, we use 3D imaging technique to conduct in-depth research in the football training, and obtain the 3D space image of the best football team. We use FPGA hardware platform to design the control program of...
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ISBN:
(纸本)9783038351153
In this paper, we use 3D imaging technique to conduct in-depth research in the football training, and obtain the 3D space image of the best football team. We use FPGA hardware platform to design the control program of 3D image, and judge the performance of synthetic parameters, and test process curve and schematic diagram of 3D imaging. Combined with kmeans algorithm we design the clustering algorithm mathematical model of 3D image, and give the control programming. Finally, based on the 3D synthesis image and optimization of display technology, using the image acquisition and skill of physical body, finally we get the best offensive and defensive football team. It provides the theory reference for the training of football players.
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