scholarly journals Online Education and Wireless Network Coordination of Electronic Music Creation and Performance under Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ning Xu ◽  
Yuanyuan Zhao
Keyword(s):  
Artificial Intelligence ◽  
Online Education ◽  
Wireless Network ◽  
Electronic Music ◽  
Clustering Algorithm ◽  
Mean Squared Error ◽  
Clustering Algorithms ◽  
Music Performance ◽  
The Mean ◽  
And Performance

This paper is aimed at studying the online education and wireless network collaboration on electronic music creation and performance under artificial intelligence (AI). This paper uses a fuzzy clustering algorithm (FCA), designs the sensor network-related equipment, and uses AI to design an electronic music creation system. The analysis of simulation experiments suggests that under the premise of increasing the number of neighbors, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of collaborative filtering and fuzzy C -means clustering algorithms show a downward trend. However, with the same number of neighbors, the filtering matching algorithm is greater than FCA regarding the mean values of MAE and MSE. Meanwhile, on the electronic music performance system of AI, the digital module is designed and the sound data are imaged on the oscilloscope, and the collaboration of electronic music online education and wireless network is completed. The following conclusion is drawn: modularizing the creative mode of intelligent electronic music has achieved higher computational efficiency. Through the oscilloscope, the sound feature is converted into the image structure, and the corresponding sound and image mode is formed, which realizes the purpose of online electronic music intelligent matching and optimizes the effect of online education. In the AI environment, the matching degree of verification electronic music curriculum resources is better than traditional matching algorithms, and the accuracy is higher.

scholarly journals An Improved Version of K-medoid Algorithm using CRO

2018 ◽  
Vol 12 (2) ◽  
pp. 116 ◽  
Author(s):  
Amjad Hudaib ◽  
Mohammad Khanafseh ◽  
Ola Surakhi
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Hybrid Algorithm ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Repository ◽  
The Mean ◽  
Real World Datasets ◽  
Actual Point ◽  
Learning Data

Clustering is the process of grouping a set of patterns into different disjoint clusters where each cluster contains the alike patterns. Many algorithms had been proposed before for clustering. K-medoid is a variant of k-mean that use an actual point in the cluster to represent it instead of the mean in the k-mean algorithm to get the outliers and reduce noise in the cluster. In order to enhance performance of k-medoid algorithm and get more accurate clusters, a hybrid algorithm is proposed which use CRO algorithm along with k-medoid. In this method, CRO is used to expand searching for the optimal medoid and enhance clustering by getting more precise results. The performance of the new algorithm is evaluated by comparing its results with five clustering algorithms, k-mean, k-medoid, DB/rand/1/bin, CRO based clustering algorithm and hybrid CRO-k-mean by using four real world datasets: Lung cancer, Iris, Breast cancer Wisconsin and Haberman’s survival from UCI machine learning data repository. The results were conducted and compared base on different metrics and show that proposed algorithm enhanced clustering technique by giving more accurate results.

scholarly journals An Improved Spectral Clustering Community Detection Algorithm Based on Probability Matrix

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Shuxia Ren ◽  
Shubo Zhang ◽  
Tao Wu
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Transition Probability ◽  
Clustering Algorithms ◽  
Detection Algorithm ◽  
Community Information ◽  
The Mean ◽  
Community Detection Algorithm ◽  
Spectral Clustering Algorithm

The similarity graphs of most spectral clustering algorithms carry lots of wrong community information. In this paper, we propose a probability matrix and a novel improved spectral clustering algorithm based on the probability matrix for community detection. First, the Markov chain is used to calculate the transition probability between nodes, and the probability matrix is constructed by the transition probability. Then, the similarity graph is constructed with the mean probability matrix. Finally, community detection is achieved by optimizing the NCut objective function. The proposed algorithm is compared with SC, WT, FG, FluidC, and SCRW on artificial networks and real networks. Experimental results show that the proposed algorithm can detect communities more accurately and has better clustering performance.

scholarly journals The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1295 ◽  
Author(s):  
Mohiuddin Ahmed ◽  
Raihan Seraj ◽  
Syed Mohammed Shamsul Islam
Keyword(s):  
Experimental Analysis ◽  
Clustering Algorithm ◽  
Fundamental Problem ◽  
Clustering Algorithms ◽  
Data Types ◽  
Data Mining Algorithms ◽  
Recent Developments ◽  
Comprehensive Survey ◽  
And Performance ◽  
Mining Algorithms

The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.

scholarly journals Evaluation of Knowledge, Attitude and Performance Regarding Gestational Diabetes Mellitus in Southwest of Iran

2019 ◽  
pp. 1-6
Author(s):  
Atefe Khanpaye ◽  
Yaghoob Madmoli ◽  
Banafsheh Riahipour ◽  
Maede Mohebifar ◽  
Mostafa Madmoli ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Online Education ◽  
Gestational Diabetes Mellitus ◽  
Information Source ◽  
Health Centers ◽  
Special Focus ◽  
Level Of Knowledge ◽  
The Mean ◽  
And Performance

Background and Aim: It seems that measuring the level of knowledge and attitude towards gestational diabetes mellitus is essential. The purpose of this study was to determine the knowledge, attitude and performance of gestational diabetes among women referring to health centers Masjed-Soleyman in 2018. Materials and Methods: In this descriptive cross-sectional study, 142 women referred to health centers in Masjed-Soleyman in Iran were evaluated by convenience sampling method. The data collection tool was a researcher-made questionnaire entitled "Assessing the level of knowledge, attitude and performance of mothers from gestational diabetes mellitus". Data analysis with spss-20 software using T-test, ANOVA and Pearson correlation coefficient was done. Results: In this study, 142 women with an average age of 38.88 ± 16.91 were studied. Of these, 47.9% were diploma, 31.7% higher than diploma and 20.4% were illiterate. The mean score of knowledge, attitude and performance of these individuals were 19.14 ± 8.94, 20.77 ± 5.71, and 8. 21. ± 3.21 that indicating good knowledge and performance and average attitude about gestational diabetes. Female employees had significantly higher knowledge, attitude and performance than others (p <0.05), But there was no significant relationship between the mean scores of knowledge, attitude and practice, number of abortions, education, gestational age and information source (p <0.05). Conclusion: The results showed that knowledge, attitude and performance in these people are at an acceptable level, but not yet ideal. It is suggested that online education programs should be developed for pregnant women with a special focus on gestational diabetes mellitus.

scholarly journals Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
He Ma ◽  
Yi Zuo ◽  
Tieshan Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Feature Representation ◽  
Trajectory Prediction ◽  
Research Issues ◽  
The Mean ◽  
Cubic Spline Curve

With the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most researchers have focused on the single-trajectory prediction of vessels. This article proposes a multiple-trajectory prediction model and makes two main contributions. First, we propose a novel method of trajectory feature representation that uses a hierarchical clustering algorithm to analyze and extract the vessel navigation behavior for multiple trajectories. Compared with the classic methods, e.g., Douglas–Peucker (DP) and least-squares cubic spline curve approximation (LCSCA) algorithms, the mean loss of trajectory features extracted by our method is approximately 0.005, and it is reduced by 50% and 30% compared to the DP and LCSCA algorithms, respectively. Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively.

scholarly journals An Improved Similarity-based Clustering Algorithm for Multi-database Mining

2021 ◽  
Author(s):  
Salim Miloudi ◽  
Yulin Wang ◽  
Wenjia Ding
Keyword(s):  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Clustering Algorithms ◽  
Back Propagation ◽  
Mean Value ◽  
Learning Rate ◽  
Database Mining ◽  
Gradient Based ◽  
Series Of Experiments ◽  
The Mean

Clustering algorithms for multi-database mining (MDM) rely on computing $(n^2-n)/2$ pairwise similarities between $n$ multiple databases to generate and evaluate $m\in[1, (n^2-n)/2]$ candidate clusterings in order to select the ideal partitioning which optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the $n$ databases in one cluster or by returning $n$ singleton clusters. To tackle the latter problem, we propose a learning algorithm to reduce the fuzziness in the similarity matrix by minimizing a weighted binary entropy loss function via gradient descent and back-propagation. As a result, the learned model will improve the certainty of the clustering algorithm by correctly identifying the optimal database clusters. Additionally, in contrast to gradient-based clustering algorithms which are sensitive to the choice of the learning rate and require more iterations to converge, we propose a learning-rate-free algorithm to assess the candidate clusterings generated on the fly in a fewer upper-bounded iterations. Through a series of experiments on multiple database samples, we show that our algorithm outperforms the existing clustering algorithms for MDM.

scholarly journals COT-16 Development of automatic lesion extraction application using artificial intelligence for the purpose of simplifying tumor volume measurement of meningioma

2021 ◽  
Vol 3 (Supplement_6) ◽  
pp. vi30-vi30
Author(s):  
Ryuichi Hirayama ◽  
Takamitsu Iwata ◽  
Shuhei Yamada ◽  
Hideki Kuroda ◽  
Tomoyoshi Nakagawa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Error Rate ◽  
Tumor Volume ◽  
Mean Squared Error ◽  
Volume Measurement ◽  
University Hospital ◽  
Evaluation Index ◽  
Imaging Biomarkers ◽  
The Mean ◽  
Tumor Volume Measurement

Abstract BACKGROUND: With the widespread use of MRI equipment and brain scans, opportunities to perform follow-up examinations for meningiomas have increased. On the other hand, an objective evaluation index for meningiomas characterized by slow changes on imaging has not been established. To establish a volume-based evaluation index for meningoceles, we are developing an application for automatic lesion extraction using artificial intelligence as a highly reproducible tumor volume measurement technique that enables large volume image data processing. METHODS: In this study, 195 patients with meningioma who underwent contrast-enhanced MRI imaging at Osaka University Hospital were included. The images were manually extracted by three neurosurgeons and used as supervised data. deeplabV3 was used as the learning network. All the supervised data were randomly divided into training (80%) and testing (20%) data, and the application was constructed by deep learning and validation with 5-fold cross-validation. The matching rate of the area of the region automatically extracted by the device against the test data and the mean square error rate of the calculated tumor volume were used as indices of the product measurement performance. RESULTS: The matching rate using the automatic extraction application for the correct data(Dice index) was 91.5% on average. The mean squared error rate of the tumor volume calculated from these extracted regions was 8.84%. CONCLUSION: We consider that this application using artificial intelligence has a certain degree of validity in terms of the accuracy of extracted lesions. In the future, it is necessary not only to improve the performance of the equipment but also to clarify the clinical significance of the new imaging biomarkers based on tumor volume that can be obtained from these lesion extraction techniques.

Clustering Algorithm for Human Behavior Recognition Based on Biosignal Analysis

2013 ◽  
pp. 212-224
Author(s):  
Neuza Nunes ◽  
Diliana Rebelo ◽  
Rodolfo Abreu ◽  
Hugo Gamboa ◽  
Ana Fred
Keyword(s):  
Time Series ◽  
Human Behavior ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Behavior Recognition ◽  
Biosignal Analysis ◽  
Synthetic Signal ◽  
The Mean ◽  
Cepstral Coefficients ◽  
Human Behavior Recognition

Time series unsupervised clustering is accurate in various domains, and there is an increased interest in time series clustering algorithms for human behavior recognition. The authors have developed an algorithm for biosignals clustering, which captures the general morphology of a signal’s cycles in one mean wave. In this chapter, they further validate and consolidate it and make a quantitative comparison with a state-of-the-art algorithm that uses distances between data’s cepstral coefficients to cluster the same biosignals. They are able to successfully replicate the cepstral coefficients algorithm, and the comparison showed that the mean wave approach is more accurate for the type of signals analyzed, having a 19% higher accuracy value. They authors also test the mean wave algorithm with biosignals with three different activities in it, and achieve an accuracy of 96.9%. Finally, they perform a noise immunity test with a synthetic signal and notice that the algorithm remains stable for signal-to-noise ratios higher than 2, only decreasing its accuracy with noise of amplitude equal to the signal. The necessary validation tests performed in this study confirmed the high accuracy level of the developed clustering algorithm for biosignals that express human behavior.

scholarly journals Optimizing MSE for Clustering with Balanced Size Constraints

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 338 ◽  
Author(s):  
Wei Tang ◽  
Yang Yang ◽  
Lanling Zeng ◽  
Yongzhao Zhan
Keyword(s):  
Clustering Algorithm ◽  
Mean Squared Error ◽  
State Of The Art ◽  
Simplex Algorithm ◽  
Linear Program ◽  
Constraint Matrix ◽  
Squared Error ◽  
Group Data ◽  
Size Constraints ◽  
The Mean

Clustering is to group data so that the observations in the same group are more similar to each other than to those in other groups. k-means is a popular clustering algorithm in data mining. Its objective is to optimize the mean squared error (MSE). The traditional k-means algorithm is not suitable for applications where the sizes of clusters need to be balanced. Given n observations, our objective is to optimize the MSE under the constraint that the observations need to be evenly divided into k clusters. In this paper, we propose an iterative method for the task of clustering with balanced size constraints. Each iteration can be split into two steps, namely an assignment step and an update step. In the assignment step, the data are evenly assigned to each cluster. The balanced assignment task here is formulated as an integer linear program (ILP), and we prove that the constraint matrix of this ILP is totally unimodular. Thus the ILP is relaxed as a linear program (LP) which can be efficiently solved with the simplex algorithm. In the update step, the new centers are updated as the centroids of the observations in the clusters. Assuming that there are n observations and the algorithm needs m iterations to converge, we show that the average time complexity of the proposed algorithm is O ( m n 1 . 65 ) – O ( m n 1 . 70 ) . Experimental results indicate that, comparing with state-of-the-art methods, the proposed algorithm is efficient in deriving more accurate clustering.

The sinkage characteristics and prediction of a planetary rover based on a similarity model experiment

2018 ◽  
Vol 233 (10) ◽  
pp. 3762-3774
Author(s):  
Han Huang ◽  
Shucai Xu ◽  
Zou Meng ◽  
Jianqiao Li ◽  
Jinhuan Zhang
Keyword(s):  
Mean Squared Error ◽  
Rear Wheel ◽  
Experimental Results ◽  
Slip Ratio ◽  
Slip Conditions ◽  
Apparent Slip ◽  
Squared Error ◽  
The Mean ◽  
And Performance ◽  
Similarity Law

The environment on an extraterrestrial planet is complex, with soft surfaces and low gravity, which make it easy for rovers to sink and skid. Excessive sinkage may occur under large slip conditions of probe rovers and could influence the survey mission. Predicting the sinkage performance of wheels under slip conditions is important for the development and performance evaluation of exploration rovers. This paper presents a dimensional analysis on the main parameters of the wheel–soil interaction system; the analysis was performed based on the similarity law, for which corresponding similar scale values were obtained. Referring to the lunar surface gravity environment, we have produced a 1/2 scaling model rover. To investigate the sinkage characteristics of the model rover, tests were performed with different wheel loads (5 N, 7 N, and 9 N) and soil states (loose, natural, and compact). The characteristic parameters of a rear wheel rut were also analyzed, including rut depth (hereinafter referred to as apparent sinkage) and slip ratio (hereinafter referred to as apparent slip ratio). Experimental results were analyzed to evaluate the sinkage characteristics and to draw conclusions. Sinkage models for the rover under different soil states were proposed, and verification and error analyses for the sinkage models were conducted using indices such as the mean relative error and root mean squared error. The experimental results and conclusions are useful for optimal rover design and improvement/verification of wheel–soil interaction mechanics models.

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