Retracted] Music Online Education Reform and Wireless Network Optimization Using Artificial Intelligence Piano Teaching

The support vector machine (SVM) combined with the K-nearest neighbor (KNN) algorithm is applied to the 3D image recognition algorithm to cope with the issues of 3D image recognition in the image processing process. In this paper, the KNN algorithm is used to extend the eigenvalues of the residual network from frequency to complex plane according to the eigenvalues of 3D images, and the relevant data are extracted from the images by using the corresponding separation rules of the SVM. Subsequently, a sparse observation model is established based on the SVM combined with the KNN, and the 3D image processing issue is converted to a residual network visualization issue of 3D image recognition by establishing a parameterized database. Meanwhile, detection of high-resolution distance of 3D images is conducted, and the variational inference method is used to visualize and analyze the 3D images accordingly. Finally, through the experimental study, it can be known that the method of SVM combined with the KNN algorithm can reach an accuracy of up to 94.18% in the dataset of the Princeton 3D polygonal models. After comparison with the existing SVM-KNN method and deep learning, it can be concluded that the method put forward in this paper has a higher recognition rate and stronger anti-interference capacity, and the algorithm has a superior convergence speed with less adjustment parameters.

Using information technology such as search engine for collecting and monitoring of network public opinion is a practical and effective method. This paper puts forward an improved algorithm of SVM classifier based on incremental learning online, and then implements a topic crawler system for network public opinion and to grab the public opinion.

Sales data analysis is an important aspect of modern businesses, as it enables them to gain insights into customer preferences and behavior, optimize marketing strategies, and improve revenue. With the rise of big data and machine learning, businesses can now leverage these tools to analyze sales data and gain even deeper insights. This paperfocuses on analyzing sales data using machine learning algorithms, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm. KNN is a popular machine learning algorithm used in classification and regression problems, and it works by identifying the K closest data points to a new observation and predicting its class or value based on the majority class or average value of its nearest neighbors. The dataset used in this paperis from a retail store and includes information on product type, sales channel, customer demographics, and sales volume over a period of several years. The paper will use Python and various machine learning libraries to perform data preprocessing, exploratory data analysis, feature engineering, and modeling. Specifically, KNN will be used to develop a predictive model for sales forecasting, which can help businesses optimize their inventory and production planning. The paper will also use clustering techniques, such as K-Means, to identify customer segments based on their purchasing behavior and demographics, and association rule mining to identify product affinities and recommend complementary products. The results of this analysis can help businesses gain a deeper understanding of their sales data and make data-driven decisions to improve their bottom line. By leveraging the power of KNN and other machine learning algorithms, businesses can gain insights into customer behavior and preferences, optimize their marketing strategies, and improve their overall revenue. Keywords: KNN Algorithm, Data Analytics, Machine Learning, Support Vector Machine.

In order to further promote the innovative development of piano teaching of music major in colleges and universities under a diversified environment, we explore a modern piano teaching reform with distinctive innovative characteristics and strengthen the work needs of colleges and universities for the cultivation of comprehensive, practical, and high-quality piano talents. This study deeply understands the current piano learning, curriculum teaching, and innovative practice of music majors in colleges and universities in the form of questionnaire and interview. On this basis, based on the summary of piano teaching methods, this paper puts forward a piano teaching curriculum system based on innovative development, hoping to provide reference and suggestions for the innovation, reform, and development of piano teaching of music majors in colleges and universities.

This research aims to categorize survey data to determine the level of satisfaction with the services provided by the village government as a public service provider. Villages or sub-districts currently offer services in response to community demand, although only partially or as efficiently as possible. The data collection technique used was distributing questionnaires to the village community. The method used for classification is the machine learning method. Before the classification process, feature selection is carried out at the data pre-processing stage using Particle Swarm Optimization (PSO), which has been proven to increase the accuracy of the classification values. The classification methods employed include Decision Tree (DT), Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms for classification purposes. This study achieves the maximum level of accuracy in decision tree classification, attaining an accuracy rate of 97.74%. Subsequently, the KNN algorithm achieved an accuracy of 77.90%, the Nave Bayes algorithm achieved 64.4%, and the SVM algorithm, which yielded the lowest accuracy value, achieved 59.90%. Following the application of Particle Swarm Optimization (PSO) for optimization, the accuracy of the SVM and KNN algorithms improved to 98.3%. The Decision Tree algorithm achieved a value of 97.77%, while the Naive Bayes technique yielded a value of 69.30%.

Rice is the primary food source for the Indonesian population, making it a priority commodity in Indonesia. Rice production plays a significant role in Indonesia's economic development, with high-yield rice varieties being crucial for enhancing national rice output. Ensuring food security requires the selection of superior rice varieties with optimal quality. This study evaluates various high-yield rice varieties, including INPARA, INPARI, INPAGO, and HIPA, based on characteristic data collected in 2023. Machine learning algorithms, increasingly central to data analysis, were applied, leveraging labeled data suitable for supervised learning methods. During the pre-processing stage, it was determined that the data did not meet the linearity assumption. Thus the Support Vector Machine (SVM) algorithm was modified with the Radial Basis Function (RBF) kernel to better handle non-linear data. Additionally, the K-Nearest Neighbor (KNN) algorithm, a traditional method, was used for comparison. The results indicate that SVM with the RBF kernel achieved faster processing times and the accuracy value reaches 96%, nearly 10% higher than the KNN algorithm.

Air pollution has become one of the biggest environmental challenges worldwide, and Jakarta, the capital city of Indonesia, is no exception. Jakarta, as one of the most populous cities in the world, faces serious problems in ensuring healthy air quality for its residents. The impact of this air pollution is not only limited to human health, but also damages the environment as a whole, including plants and aquatic ecosystems. In an effort to address this issue, many studies have been conducted to develop prediction models that can provide estimates of air quality levels in Jakarta City. Two modeling models that can be used in this context are Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). This study aims to compare the performance of the SVM algorithm model with the KNN algorithm in the classification of air quality in Jakarta City. Data mining is the art and science of discovering knowledge, insights, and patterns in data based on the CRISP -DM (Cross Industry Standard Process For Data Mining) methodology. The data source in this study is the Jakarta air pollution standard index (ISPU) data obtained on the satudata.jakarta.go.id website. This study compares the accuracy of the Jakarta City air quality classification on the Support vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms using python. Comparison results are determined by the level of accuracy and score of the confusion matrix. Classification results are presented in the form of a Graphic User Interface with media interface. The comparison of classification algorithms on two models in data mining shows that the Support Vector Machine (SVM) algorithm is superior to K-Nearest Neighbor (KNN). This is evident from the higher accuracy rate in SVM, especially with the use of the Rbf kernel which reaches 97.05%, compared to KNN which has an accuracy of 94.74% with parameters p = 1 and k = 5. In addition, SVM also shows a higher correct prediction value compared to KNN. In a 1-year time span the overall quality may be quite good, with moderate quality at more than equal to 50 to 100. However, control is still needed for the unhealthy category areas.

Abstract. This system was developed to determine the accuracy of sentiment analysis on Twitter regarding the COVID-19 issue using the Nazief-Adriani and Porter stemmers with TF-IDF weighting, along with a classification process using K-Nearest Neighbor (KNN) that resulted in a comparison of 48.24% for Nazief-Adriani and 48.24% for Porter. Purpose: This research aims to determine the accuracy of the Nazief-Adriani and Porter stemmer algorithms in performing text preprocessing using a dataset from Indonesian-language Twitter. This research involves word weighting using TF-IDF and classification using the K-Nearest Neighbor (KNN) algorithm. Methods/Study design/approach: The experimentation was conducted by applying the Nazief-Adriani and Porter stemmer algorithm methods, utilizing data sourced from Twitter related to COVID-19. Subsequently, the data underwent text preprocessing, stemming, TF-IDF weighting, accuracy testing of training and testing data using K-Nearest Neighbor (KNN) algorithm, and the accuracy of both stemmers was calculated employing a confusion matrix table. Result/Findings: This study obtained reasonably accurate results in testing the Nazief-Adriani stemmer with an accuracy of 50.98%, applied to sentiment analysis of COVID-19-related Twitter data using the Indonesian language. As for the accuracy of the Porter stemmer, it achieved an accuracy rate of 48.24%. Novelty/Originality/Value: Feature selection is crucial in stemmer accuracy testing. Therefore, in this study, feature selection is carried out using the Nazief-Adriani and Porter stemmers for testing purposes, and the accuracy data classification is conducted using the K-Nearest Neighbor (KNN) algorithm

This study compares the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms in predicting customer satisfaction at Warung Makan Indomie (Warmindo). The research process consists of four stages, namely: data collection, data processing, model formation, and model evaluation. This study aims to compare the performance of two classification algorithms, namely K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), in predicting customer satisfaction levels based on survey data. The evaluation was carried out using accuracy metrics and classification reports to determine the level of precision, recall, and f1-score of each algorithm. The evaluation results show that both algorithms have the same accuracy of 70%. KNN excels in f1-score in class 2 (0.70), while SVM excels in precision in class 2 (0.79). with an average score of both algorithms being 0.61. These results indicate that both KNN and SVM are feasible to use, depending on the performance priority per class

Distributed Denial of Service (DDoS) attacks represent a significant danger in network security because they can lead to extensive service interruptions. With these attacks increasingly mirroring regular traffic, smart and effective detection systems are essential. This research seeks to assess the efficacy of the Support Vector Machine (SVM) classification algorithm in identifying DDoS attacks in network traffic. The data utilized is CICIDS2017, focusing on the subset Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv, which contains both legitimate traffic and DDoS attacks like DoS-Hulk, DoS-GoldenEye, and DDoS. The preprocessing stage included eliminating duplicates and null entries, label binary encoding, normalization through Min-Max Scaler, and feature selection applying the Chi-Square technique. The data was divided into 80% for training and 20% for testing purposes. The Radial Basis Function (RBF) kernel was utilized to train the SVM model, and hyperparameter optimization was performed with GridSearchCV. The evaluation of the model's performance was conducted through accuracy, precision, recall, F1-score, confusion matrix, and visual representations including ROC and Precision-Recall Curves. The findings indicate that prior to tuning, the model reached an accuracy of 97%, which increased to 99% post-tuning, accompanied by an F1-score of 0.99. This shows that the SVM algorithm, when paired with appropriate preprocessing and optimization, is very efficient in identifying DDoS attacks within network traffic.

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches (such as the k-nearest neighbor (KNN) algorithm). In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine (SVM) methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding zphot. Our analysis has found two dense regions with catastrophic failure, one in the range of zphot ϵ [0.3, 1.2] and the other in the range of zphot ϵ [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey (SDSS) quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different |δz| ranges and root mean square (rms) error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN (71.96%, 83.78%, 89.73% and 0.204).

To solve the problem that traditional multi-label support vector machine (SVM) classification algorithm adopting nonlinear kernel has been severely restricted from being used on large-scale data sets, we propose fast multi-label low-rank-linearized SVM classification algorithm based on approximate extreme points (AEML-LLSVM). First, it adopts the approximate extreme points' method to obtain representative sets from the training data set. Then, the approximate extreme points' low-rank-linearized SVM (AELLSVM) is trained on the representative sets. The AELLSVM integrates the advantages of approximate extreme points' method and LLSVM. Experimental results on three large-scale multi-label data sets have proven that the training and the testing speed of AEML-LLSVM classification algorithm are greatly improved under the premise that its classification performance is similar to that of ML-LIBSVM classification algorithm and superior to that of other fast multi-label SVM classification algorithms.

Every time a new variant of Coronavirus (Covid-19) appears, themedia or news platforms review it to find out whether the new variantis more dangerous or contagious than before. One of the media orplatforms that is fast in presenting news in videos is YouTube.YouTube is a social media that can upload videos, watch videos, andcomment on the video. The comment field on YouTube videos cannotbe separated from spam comments that annoy other users who want tofollow or participate in the comment column. Indication of spamcomments is still done by observing one by one; this is very inefficientand time-consuming. This study aims to create a model that canclassify spam on YouTube comments. The classification method uses the SVM (Support Vector Machines) algorithm and the KNN (K-Nearest Neighbor) algorithm to identify spam comments or not with comment data taken from Omicron's Covid-19 news video on national news channels. The classification results show that the SVM method is superior inaccuracy with the Linear SVC algorithm of 75.12%, SVC of 76.11%, and Nu-SVC of 77.11%. While the KNN algorithm with k=2 is 65.67%, k=4 is 64.51%, k=6 is 62.35%.

The brain–computer interface (BCI) is a direct communication channel between humans and machines that relies on the central nervous system. Neuroelectric signals are collected by placing electrodes, and after feature sampling and classification, they are converted into control signals to control external mechanical devices. BCIs based on steady-state visual evoked potential (SSVEP) have the advantages of high classification accuracy, fast information conduction rate, and relatively strong anti-interference ability, so they have been widely noticed and discussed. From k-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM) classification algorithms to the current deep learning classification algorithms based on neural networks, a wide variety of discussions and analyses have been conducted by numerous researchers. This article summarizes more than 60 SSVEP- and BCI-related articles published between 2015 and 2023, and provides an in-depth research and analysis of SSVEP-BCI. The survey in this article can save a lot of time for scholars in understanding the progress of SSVEP-BCI research and deep learning, and it is an important guide for designing and selecting SSVEP-BCI classification algorithms.

The security and integrity of computer systems and networks highly depend on malware detection. In the realm of malware detection, the K-Nearest Neighbors (KNN) algorithm is a well-liked and successful machine learning algorithm. However, the choice of an acceptable distance metric parameter has a significant impact on the KNN algorithm's performance. This study tries to improve malware detection by adjusting the KNN algorithm's distance metric parameter. The distance metric greatly influences the similarity or dissimilarity between instances in the feature space. The KNN algorithm for malware detection can be more accurate and effective by carefully choosing or modifying the distance metric. This paper analyzes multiple distance metrics, including Minkowski distance, Manhattan distance, and Euclidean distance. These metrics account for the traits of malware samples while capturing various aspects of similarity. The effectiveness of the KNN algorithm is evaluated using the MalMem-2022 malware dataset, and the results are broken down into these three-distance metrics. The experimental findings show that, among the three distance metric parameters, the Euclidean and Minkowski distance metric parameters considerably produced the best outcomes with binary classification. While with multiclass classification, the KNN algorithm has achieved the highest outcomes using Manhattan distance.