A Novel Metric for Detecting Anomalous Ship Behavior Using a Variation of the DBSCAN Clustering Algorithm

Botts, Carsten H.

doi:10.1007/s42979-021-00804-4

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…Figure 1 shows how American students enhance their reading skills. Figure 1 Ways U.S. Students Improve Reading Skills (2) The large proportion of reading comprehension questions in various college English exams determines the importance of strengthening college English reading After China's accession to the WTO, English has become the "bottleneck" for China to go global [29,30]. At present, most colleges and universities link CET-4 and CET-6 certificates to degree certificates, and enterprises also use whether they hold CET-4 and CET-6 certificates as an important criterion for selecting resumes when recruiting talents, resulting in CET-4 and CET-6 exams that are almost beyond the level of the university period.…”

Section: Overview Of English Reading Abilitymentioning

confidence: 99%

“…Someone once said: "A student who does not pay attention to reading is a student who lacks the ability to acquire knowledge and develop potential; a family that does not pay attention to reading is a family with shallow cultural connotations and mediocre; a nation that does not pay attention to reading must be a family without A nation of hope [1]." Through reading people feel more thoughts and emotions of others, experience more happiness; through reading people know more about human beings, transcend time and space, and transcend the constraints of self [2]. Reading in modern society is not only a learning ability, but also a survival ability [3].…”

Section: Introductionmentioning

confidence: 99%

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Application of clustering algorithm in the evaluation of college students' English reading ability under the framework of big data

Wang

2024

Preprint

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In recent years, China has accelerated its internationalization process and made more and more achievements in cross-country exchanges and cooperation. This also means that China's demand for foreign language talents is increasing. Currently, there are two prominent problems in college students 'English learning: one is the personalized problem. For students with different abilities, they cannot teach students according to their aptitude. At the same time, they have made a learning plan, but in the later stage, they do not adjust the learning plan according to the growth of students' learning ability and knowledge from the adaptation. Therefore, learning English is very important for contemporary college students. And English reading is an important means to acquire English language knowledge, understand external information and improve English language practice ability. Therefore, cultivating college students' English reading ability is a pivotal content of college English teaching. With the advancement of English reform, the theory of English reading teaching has been updated all the time. English reading ability is not only the basis of English comprehensive ability. Training students' reading ability greatly promotes the improvement of College Students' Comprehensive English level. Teachers should also teach students by aptitude to improve students' English reading ability.

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Section: Overview Of English Reading Abilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of clustering algorithm in the evaluation of college students' English reading ability under the framework of big data

Wang

2024

Preprint

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“…Wibisono et al 15 presented a multivariate weather anomaly detection method in which they utilized the DBSCAN algorithm using multiple weather variables to cluster anomaly data points from normal data points. Botts 16 designed a unique metric for detecting outlier behavior in a ship's automatic identification system (AIS) data using the DBSCAN algorithm. Sheridan et al 17 utilize DBSCAN in a flight anomaly detection system.…”

Section: Related Workmentioning

confidence: 99%

Effectively predicting cyber‐attacks through isolation forest learning‐based outlier detection

Ripan

Islam

Alqahtani

et al. 2022

Security and Privacy

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Due to the popularity of Internet of Things devices, the exponential progress of computer networks, and a plethora of associated applications, cybersecurity has recently attracted much attention in light of today's security problems. As a result, detecting various cyber‐attacks within a network and developing an effective cyber‐attacks prediction model that plays a crucial part in today's defense has become increasingly critical. Modeling cyber‐attacks effectively, on the other hand, is challenging because modern security datasets hold a large number of dimensions of security features and may contain outliers. To accomplish this, we provide an approach for categorizing cyber‐attacks effectively through isolation forest learning‐based outlier detection. Additionally, we apply a variety of popular machine learning approaches to assess the performance of cyber‐attacks prediction models, including logistic regression, support vector machine, AdaBoost classifier, naive Bayes, and K‐nearest neighbor. We evaluated the efficacy of our approach by running tests on three network intrusion datasets (KDD Cup 99, CIC‐IDS2017, and UNSW‐NB15) and computing the precision, recall, and accuracy. Experiments demonstrate that eliminating outliers improves the prediction accuracy of cyber‐attacks for different classifiers. Additionally, we compare the isolation forest learning‐based outlier detection model to other well‐known outlier detection techniques, DBSCAN and k‐means, and measure the effectiveness of our model.

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“…Wireless Communications and Mobile Computing of a large amount of game data is still time-consuming and laborious, and complete intelligent collection has not been achieved. Using artificial intelligence technology as an important way to strive to achieve intelligent training and competition plays an important role in improving the sports potential of athletes and transforms it into sports performance through competition [14]. Through the Center Net target detection technology, the game skill detection, tactical analysis, and diagnosis in tennis matches are used as the judging criteria.…”

Section: Tennis Net Recognition Technology In Machine Learningmentioning

confidence: 99%

Artificial Intelligence Technology to Record the Number of Times the Ball Passes the Net in Tennis Matches

Liu

Huang

2022

Wireless Communications and Mobile Computing

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Tennis competition is one of sports events. The vigorous development of sports can promote people’s pursuit of sports spirit and promote social unity and stability. However, in the course of social modernization development, sports events also need to follow closely, and we can no longer rely on traditional tools to support the development of events, which is not conducive to the positive development of sports. Due to the problem that the number of times the ball crosses the net in the traditional tennis game is too backward and the error rate is high, this paper uses artificial intelligence technology to record the number of times the ball crosses the net in tennis games and introduces the Center Net target based on the diagnostic criteria and comprehensive evaluation of tennis nets. Detect and track tennis balls by target recognition, feature extraction, and other methods, and record the times of net passing. However, considering that abnormal behaviors will also occur in the process of tennis passing through the net, the density-based DBSCAN clustering algorithm is used to discriminate and record abnormal behaviors in tennis matches. In order to verify the detection performance of DBSCAN clustering algorithm and Center Net target detection, the video of tennis match was analyzed and compared by drone from the perspective of time and space. By recording the number of times the ball crosses the net during the tennis receiving, connecting and attacking, and stalemate phases, the performance of the two recording times is compared. R 2 are all higher than 0.94, and R values are 0.982 and 0.963, respectively, so the recall rate of DBSCAN clustering algorithm is 0.02 higher, which is better than Center Net target detection. Using artificial intelligence technology to record the number of times the ball crosses the net in tennis games can not only improve the scientificity and accuracy of the record but also promote the development of the sports industry.

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A Novel Metric for Detecting Anomalous Ship Behavior Using a Variation of the DBSCAN Clustering Algorithm

Cited by 7 publications

References 16 publications

Application of clustering algorithm in the evaluation of college students' English reading ability under the framework of big data

Application of clustering algorithm in the evaluation of college students' English reading ability under the framework of big data

Effectively predicting cyber‐attacks through isolation forest learning‐based outlier detection

Artificial Intelligence Technology to Record the Number of Times the Ball Passes the Net in Tennis Matches

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