scholarly journals Prediction of Volleyball Competition Using Machine Learning and Edge Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qiang Liu ◽  
Qiannan Liu
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Science ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Data Mining Algorithm ◽  
Human Society ◽  
Apriori Algorithm ◽  
Proposed Model ◽  
The Way

Data analysis and machine learning are the backbones of the current era. Human society has entered machine learning and data science that increases the data capacity. It has been widely acknowledged that not only does the number of information increase exponentially, but also the way of human information management and processing is completed to be changed from manual to computer, mainly depending on the transformation of information technology including a computer, network, and communication. This paper is aimed at a solution to the lag of the methods and means of volleyball technique prediction in China. Through field visits, it is found that the way of analysis and research of techniques and tactics in Chinese volleyball practice is relatively backward, which to a certain extent affected the rapid development of Chinese volleyball. Therefore, it is a necessary and urgent task to realize the reform of the methods and means of volleyball technical and tactical analysis in China. The data analysis and prediction are based on the machine learning and data mining algorithm applied to volleyball in this paper is an inevitable trend. The proposed model is applied to the data produced at the edges of the systems and thoroughly analyzed. The Apriori algorithm of the machine learning algorithm is utilized to process the data and provide a prediction about the strategies of a volleyball match. The Apriori algorithm of machine learning is also optimized to perform better data analysis. The effectiveness of the proposed model is also highlighted.

scholarly journals Improvement of Support Vector Machine Algorithm in Big Data Background

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Babacar Gaye ◽  
Dezheng Zhang ◽  
Aziguli Wulamu
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Time Complexity ◽  
Dual Problem ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Original Space

With the rapid development of the Internet and the rapid development of big data analysis technology, data mining has played a positive role in promoting industry and academia. Classification is an important problem in data mining. This paper explores the background and theory of support vector machines (SVM) in data mining classification algorithms and analyzes and summarizes the research status of various improved methods of SVM. According to the scale and characteristics of the data, different solution spaces are selected, and the solution of the dual problem is transformed into the classification surface of the original space to improve the algorithm speed. Research Process. Incorporating fuzzy membership into multicore learning, it is found that the time complexity of the original problem is determined by the dimension, and the time complexity of the dual problem is determined by the quantity, and the dimension and quantity constitute the scale of the data, so it can be based on the scale of the data Features Choose different solution spaces. The algorithm speed can be improved by transforming the solution of the dual problem into the classification surface of the original space. Conclusion. By improving the calculation rate of traditional machine learning algorithms, it is concluded that the accuracy of the fitting prediction between the predicted data and the actual value is as high as 98%, which can make the traditional machine learning algorithm meet the requirements of the big data era. It can be widely used in the context of big data.

scholarly journals ANALISIS DESKRIPTIF TINGKAT KEPERCAYAAN PELANGGAN PT. TELEKOMUNIKASI INDONESIA, Tbk BERDASARKAN DIMENSI INTEGRITY

EDUTECH ◽  
2018 ◽  
Vol 17 (1) ◽  
pp. 81
Author(s):  
Imam Soedardji ◽  
Diah Febrina
Keyword(s):  
Data Analysis ◽  
Public Relations ◽  
Rapid Development ◽  
Moderate Level ◽  
The Internet ◽  
Research Instrument ◽  
Reliability Indicators ◽  
The Way

Abstrak. Perkembangan internet yang cepat telah mengubah cara kehumasan berhub-ungan dengan masyarakat. Hal ini diikuti oleh fenomena dunia yang tanpa batas dimana tidak ada batasan bagi pengguna untuk memberikan respon terhadap suatu produk atau layanan yang berdampak pada kepercayaan yang telah dibangun selama bertahun-tahun oleh perusahaan dan publiknya. Petisi tentang IndiHome pada situs change.org mengindikasikan bahwa pelanggan me-rasa kecewa dan tidak lagi percaya pada Telkom. Penelitian ini menggunakan metode deskriptif kuantitatif dan kuesioner sebagai instrumen yang diberikan pada pelanggan dan pendukung petisi IndiHome pada situs change.org. Berdasarkan temuan dan analisis data, tingkat kepercayaan pelanggan pada Telkom jika dikaitkan dengan integritas dapat diketahui berada pada tingkat se-dang. Sekalipun demikian salah satu indikator integritas, yakni kejujuran memiliki penilaian negatif paling tinggi bila dibandingkan dengan indikator pemenuhan informasi maupun kehanda-lan. Data yang diperoleh dianalisis menggunakan tabel distribusi frekuensi dan kemudian dibagi ke dalam tiga kategori. Hasil penelitian menunjukkan bahwa dimensi integritas berada pada level moderat. Abtract. The rapid development of the internet changes the way of public relations get con-nected with their public. It also followed by “borderless” world which means limitless on users to argue about a product or service that may result in damage to the trust that has been established for many years by the company and their publics. Petition about IndiHome on Change.org means if customers feel disappointed and can’t longer believe Telkom. This study used quantitative de-scriptive research with questionnaire as a research instrument and distributed to consumers and supporter of petition about IndiHome on Change.org. Based on the findings and data analysis, the level of customers' trust on Telkom in relation to integrity is at a moderate level. Yet, one of the indicators of integrity, that is honesty, has the highest negative rating compared to information fulfillment and reliability indicators. The data was analyzed using table of distribution of frequen-cy and then recoded into three categories. The findings suggest that the dimension of integrity is at moderate level.

scholarly journals Ai4Truth: An In-depth Analysis on Misinformation using Machine Learning and Data Science

2021 ◽  
Author(s):  
Kevin Qu ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Social Issues ◽  
Fake News ◽  
Future Studies ◽  
The Public ◽  
Applied Machine Learning ◽  
Depth Analysis ◽  
Real Challenge ◽  
The Way

A number of social issues have been grown due to the increasing amount of “fake news”. With the inevitable exposure to this misinformation, it has become a real challenge for the public to process the correct truth and knowledge with accuracy. In this paper, we have applied machine learning to investigate the correlations between the information and the way people treat it. With enough data, we are able to safely and accurately predict which groups are most vulnerable to misinformation. In addition, we realized that the structure of the survey itself could help with future studies, and the method by which the news articles are presented, and the news articles itself also contributes to the result.

scholarly journals An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Aurelle Tchagna Kouanou ◽  
Thomas Mih Attia ◽  
Cyrille Feudjio ◽  
Anges Fleurio Djeumo ◽  
Adèle Ngo Mouelas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
High Rate ◽  
Supervised Machine Learning ◽  
Polymerase Chain Reaction Test ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Test Results

Background and Objective. To mitigate the spread of the virus responsible for COVID-19, known as SARS-CoV-2, there is an urgent need for massive population testing. Due to the constant shortage of PCR (polymerase chain reaction) test reagents, which are the tests for COVID-19 by excellence, several medical centers have opted for immunological tests to look for the presence of antibodies produced against this virus. However, these tests have a high rate of false positives (positive but actually negative test results) and false negatives (negative but actually positive test results) and are therefore not always reliable. In this paper, we proposed a solution based on Data Analysis and Machine Learning to detect COVID-19 infections. Methods. Our analysis and machine learning algorithm is based on most cited two clinical datasets from the literature: one from San Raffaele Hospital Milan Italia and the other from Hospital Israelita Albert Einstein São Paulo Brasilia. The datasets were processed to select the best features that most influence the target, and it turned out that almost all of them are blood parameters. EDA (Exploratory Data Analysis) methods were applied to the datasets, and a comparative study of supervised machine learning models was done, after which the support vector machine (SVM) was selected as the one with the best performance. Results. SVM being the best performant is used as our proposed supervised machine learning algorithm. An accuracy of 99.29%, sensitivity of 92.79%, and specificity of 100% were obtained with the dataset from Kaggle (https://www.kaggle.com/einsteindata4u/covid19) after applying optimization to SVM. The same procedure and work were performed with the dataset taken from San Raffaele Hospital (https://zenodo.org/record/3886927#.YIluB5AzbMV). Once more, the SVM presented the best performance among other machine learning algorithms, and 92.86%, 93.55%, and 90.91% for accuracy, sensitivity, and specificity, respectively, were obtained. Conclusion. The obtained results, when compared with others from the literature based on these same datasets, are superior, leading us to conclude that our proposed solution is reliable for the COVID-19 diagnosis.

scholarly journals Data Visualization on Crime Against Women

2019 ◽  
Vol 8 (2S4) ◽  
pp. 83-85
Keyword(s):  
Machine Learning ◽  
Data Visualization ◽  
Data Science ◽  
Learning Algorithm ◽  
Current Data ◽  
Machine Learning Algorithm ◽  
Graphical Approach ◽  
Crime Data ◽  
Statistical Variance

Visualization ensures the modern expectation of all forms of data. It is important to understand the data and its statistical variance graphically. Visualization on crime data would be supportive to analyze and prevent the threats in society. According to recent surveys and records, India has undergone many crime issues which occur on women. In order to prevent and analyze the crime issues against women, Data visualization is a useful approach to deal with it. The current data technologies available are appropriate to accomplish the task of visualization for women safety. Efficient visualization with effective machine learning algorithm and its performance finds the response for data related requests in the field of data science. This paper clarifies the details of crime against women through a graphical approach and illustrates about how to notify the unsafe levels by alert to safeguard the women

scholarly journals A New Early Rumor Detection Model Based on BiGRU Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiangning Chen ◽  
Caiyun Wang ◽  
Dong Li ◽  
Xuemei Sun
Keyword(s):  
Computer Technology ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Social Economy ◽  
Research Topic ◽  
Data Standardization ◽  
Detection Model ◽  
Proposed Model ◽  
The Social ◽  
Rumor Detection

With the progress of society and the rapid development of computer technology, rumors arise on social media, which seriously affects the social economy. How to detect rumors accurately and rapidly has become one hot research topic. In this paper, a new early rumor detection model is proposed. The aim of this model is to increase the efficiency and the accuracy of rumor detection simultaneously. Specifically, in this model, the input data is firstly refined through account filtering and data standardization, then the BiGRU is used to consider the context relationship, and a reinforcement learning algorithm is applied to detection. Experimental results show that compared with other early rumor detection models (e.g., checkpoints), the accuracy of the proposed model is improved by 0.5% with the same speed, which testifies the effectiveness of this model.

Detection and prediction of pluvial flood using machine learning techniques

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
K.A. Oladapo ◽  
F.Y. Ayankoya ◽  
F.A. Adekunle ◽  
S.A. Idowu
Keyword(s):  
Machine Learning ◽  
Flood Risk ◽  
Scientific Method ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Machine Learning Techniques ◽  
Emergency Situations ◽  
Learning Techniques ◽  
Proposed Model ◽  
Flood Detection

The periodical occurrence of emergency situations represents an important issue for mankind. Over the years, the world at large has experienced multiple misadventures both natural and man-made. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. Handling flood risk with the intention of safety and comfort of the citizens as well as saving their environment is one of the major responsibilities of the leadership in each country especially in flood prone areas. Machine learning predictive analytic applications can improve the risk management. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification to appraise the performance of the machine learning algorithm on pluvial flood conditioning variables.

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