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Monitoring, profiling and classification of urban environmental noise using sound characteristics and the knn algorithm

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ENERGY REPORTS 2020年 6卷 223-230页

作者： Tsalera, Eleni Papadakis, Andreas Samarakou, Maria Univ West Attica Dept Informat & Comp Engn Egaleo 12243 Greece Sch Pedag & Technol Educ Dept Elect & Elect Engn Educators Athens 14122 Greece

Environmental noise is a key factor affecting the quality of life in modern societies as they influence an extended set of human activities. Unwanted sounds, typically characterized as noise, can be of many types and vary in their impact and the ways to be confronted on behalf of competent public authorities. In this work, we describe environmental noise in a qualitative manner using sound-specific features from the time and spectral domains. These features consist of 8 temporal (including RMS, standard deviation, Zero Crossing Rate), 11 spectral (including spectral envelope slope, skewness, spectrum mass center, peak amplitude crest, spread and skewness) and 4 perceptual (including Mel Frequency Cepstral Coefficients) descriptors. Based upon a set of 8 discriminant types of unwanted sounds, typically met in urban environments (car horn, children playing, dog barking, drilling, engine idling, jack hammer, siren and street music), we specify a methodology of matching environmental noise into these categories. Using training and test data from the UrbanSound8K public dataset, we use the K-Nearest Neighbors (knn) algorithm for classification. The algorithm has been configured to allow from 1 to 3 neighbors, while three distance metrics (Euclidean, Chebyshev and cosine) have been employed to create 9 models that achieve performance between 70% and 85%. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Urban noise Sound classification Supervised machine learning knn algorithm

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Quantitative Timing Strategy Model Based on Improved knn algorithm

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Procedia Computer Science 2022年 202卷 61-66页

作者： Bingjie Lou Bo Shaoa Chenchen Ni Yixuan Dong Shuning Yue Ming Zhu Zhejiang International Studies University 299 Liuhe Road Xihu District Hangzhou City Zhejiang Province China Hang Zhou 310000 China

In this paper, given the mindset of data analysis, we adopt the knn algorithm based on the data-driven principle to research and judge the stock price trend. We use a weighted modified Euclidean distance algorithm to measure the distance between each feature value and the target to be measured, calculate the neighborhood K values using cross-validation, and train the *** decided to use a combination of indicators, KDJ and RSI to establish a timing strategy based on the principle of "volume and price matching", and set a stop-loss point to control the risk in order to maximize the stock return and minimize the risk.

关键词： Machine Learning SPSS Multivariate statistical analysis principal component analysis model knn algorithm

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Application Research of knn algorithm Based on Clustering in Big Data Talent Demand Information Classification

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第6期34卷

作者： Xiao, Qingtao Zhong, Xin Zhong, Chenghua Army Mil Univ Vocat Educ Ctr Chongqing Peoples R China Chongqing Technol & Business Univ Mental Hlth Educ & Counseling Ctr Chongqing Peoples R China Chongqing Technol & Business Univ Coll Environm & Resources Chongqing Peoples R China

With the growth of massive data in the current mobile Internet, network recruitment is gradually growing into a new recruitment channel. How to effectively mine available information in the massive network recruitment data has become the technical bottleneck of current education and social supply and demand development. The renewal of talent demand information is carried out every day, which produces a large amount of text data. How to manage these talents' demand information reasonably becomes more and more important. Artificial classification is time-consuming and laborious, which is unrealistic naturally. Therefore, using automatic text categorization technology to classify and manage this information becomes particularly important. To break through the bottleneck of this technology, a heuristic knn text categorization algorithm based on ABC (artificial bee colony) is proposed to adjust the weight of features, and the similarity between test observation and training observation is measured by using the method of fuzzy distance measurement. Firstly, the recruitment information is segmented and feature selection and noise data elimination are carried out by using term frequency-inverse document frequency (TF-IDF) algorithm and AP (affinity propagation) clustering algorithm. Finally, the text information is classified by using knn algorithm combined with heuristic search and fuzzy distance measurement. The experimental results show that this method effectively solves the problem of poor stability and low classification accuracy of traditional knn algorithm in text categorization method for talent demand.

关键词： Clustering knn algorithm artificial intelligence information classification

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Development of computer aided classroom teaching system based on machine learning prediction and artificial intelligence knn algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1879-1890页

作者： Quan, Yu Lingnan Normal Univ Zhanjian 524048 Guangdong Peoples R China

With the continuous development of science and technology, computer-aided teaching has become a common mode of school teaching. From the current situation, it can be seen that the current computer-aided teaching mostly replaces the traditional teaching mode with multimedia, and does not play the role of functional teaching, and teachers cannot effectively grasp the students' psychological thoughts in teaching. Based on this, this study combines machine learning prediction and artificial intelligence knn algorithm to actual teaching. Moreover, this study collects video and instructional images for student feature behavior recognition, and distinguishes individual features from group feature recognition, and can detect student expression recognition in detail. In addition, this study designed a case study to analyze the performance of the algorithm. From the experimental results, it can be seen that the proposed algorithm has certain effects and can be used as an algorithm to assist the teaching process and can provide theoretical reference for subsequent related research.

关键词： Machine learning prediction artificial intelligence knn algorithm auxiliary teaching feature recognition

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RETRACTED: Research on English grammar recognition system based on combination of genetic algorithm and knn algorithm (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期38卷 7291-7302页

作者： Wanni, Mo Guangxi Univ Nationalities Xiangsihu Coll Nanning Peoples R China

It is a very challenging task to develop a grammatical error correction model with many types of error correction and high accuracy of error correction for various English grammar mistakes. Based on Chinese English learners, this paper analyzes the grammatical mistakes that they often make, and then studies and implements an English grammar correction model with high precision and many types of error correction. Moreover, this study combines genetic algorithm and knn algorithm to construct a smart English grammar recognition system. At the same time, on the basis of summarizing the work of predecessors, this paper, based on advanced theoretical and technical knowledge, designs a grammatical error correction model based on rules and statistics. In addition, for the corresponding related word errors, this study proposes an error correction model of related word grammar. Finally, this study selects the features of artificially extracted English related words for error correction, and finally selects the optimal feature subset to improve the accuracy of model error correction.

关键词： Genetic algorithm knn algorithm English grammar feature recognition

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Anomaly detection of bridge health monitoring data based on knn algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5243-5252页

作者： Lei, Zhen Zhu, Liang Fang, Youliang Li, Xiaolei Liu, Beizhan Hebei Univ Hebei Prov Civil Engn Monitoring & Evaluat Techno Baoding 071002 Hebei Peoples R China Hebei Univ Sch Cyber Secur & Comp Sci Baoding 071002 Hebei Peoples R China Hebei Univ Coll Civil Engn & Architecture Baoding Hebei Peoples R China

Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on knn distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the abnormal mode according to the knn method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data.

关键词： Artificial intelligence bridge health monitoring data anomaly detection knn algorithm multivariate time series

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Realization of optical ADDER circuit using photonic structure and knn algorithm

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OPTIK 2020年 212卷 164675-164675页

作者： Nayak, SuryaKanta Panda, M. Palai, G. Utkal Univ Dept Comp Sci & Applicat Bhubaneswar India Gandhi Inst Technol Adv GITA Dept Elect & Commun Engn Bhubaneswar India

Nevertheless a meticulous research on photonic integrated circuit have been focused since a last decade, the present research brings a different type of approach to envisage optical ADDER circuit. Further the novelty of present research deals with the realization of photonic based ADDER circuit with the use of minimum number optical elements, where operational mechanism relies on the logic gate operation. Further plane wave explanation method is applied here to study the photonic band gap analysis to understand the logic gate operation, where the minimization of photonic structure can be made through knn algorithm. To sum up; the present paper claims that the optical ADDER circuit could be realized by using the minimum number of photonic structures.

关键词： Optical ADDER knn algorithm 2D photonic structure

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Locating a γ-ray source using cuboid scintillators and the knn algorithm

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2021年 993卷 165069-165069页

作者： Du, Te Zhao, Zijia Zhu, Qingjun Tian, Lichao Natl Univ Def Technol Coll Liberal Arts & Sci Changsha Hunan Peoples R China Chinese Acad Sci Inst Plasma Phys Hefei Anhui Peoples R China

In using radiation detectors to locate. gamma-ray sources, one needs to consider not only the detection efficiency, but also the portability of the detector and the detectable energy range. In this paper, we put forward a novel proposal which combines the advantages of cuboid scintillators and array detectors, in order to achieve portability and efficiency. To this aim, we first developed a mathematical angular response model of cuboid scintillators, and then verified the model experimentally. Then, we designed a simple structured detector array and used the Monte Carlo simulations to verify its angular resolution. Monte Carlo results show that the angular resolution of the detector array may achieve one degree. Finally, in order to further improve the detector performance, we implemented the knn algorithm, achieving an angular resolution of the order of one tenth of a degree.

关键词： gamma-ray Source location Scintillator Detector array knn algorithm

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Empirical Research on the Effect of Collaborative Learning in Blended Learning Mode Based on knn algorithm 2020

Empirical Research on the Effect of Collaborative Learning i...

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8th International Conference on Information and Education Technology (ICIET)

作者： Tan, Lin Chen, Yali Yang, Runhan Lai, Li Southwest Petr Univ Sch Sci Chengdu Sichuan Peoples R China

ISBN: (纸本)9781450377058

The quality of collaborative learning is one of the essential factors that determine the quality of teaching. Therefore, it is a significant work for educators to explore scientific and reasonable grouping methods. In this paper, first we design a Blended Learning mode in which there are a variety of online and offline learning activities. The quantified learning behavior information becomes the original data and basis for grouping. Then we combined knn (k-Nearest Neighbor) algorithm and grouping principle to implement grouping for the pilot class. Finally, the effect of this grouping method is demonstrated by comparing the final examination results and analyzing the number of students who have finished the preview. The results show that the class with the new grouping method has achieved good performance in the final examination.

关键词： Blended Learning Collaborative Learning knn algorithm

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Detection and Analysis of Network Intrusion Data Set Based on knn algorithm

World Scientific Research Journal

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World Scientific Research Journal 2021年第6期7卷 118-123页

作者： Xiaojie Ma Xiaorong Cheng

With the continuous development of the network society and the frequent occurrence of network attacks, people's demand for network intrusion detection is increasing. The method of intrusion detection is basically to design a classifier that can distinguish the normal and abnormal data in the data stream, so as to realize the alarm of the attack behavior. This article will use the KDD99 data set in the academic circle to test the quality of intrusion detection algorithms to provide a unified performance evaluation benchmark for intrusion detection systems. This article will build a classifier based on the knn algorithm, and use the 10% training set in the data set to train the classifier, and then use the corrected test set to test the classifier performance.

关键词： Network intrusion detection Data analysis knn algorithm

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