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Journal articles on the topic 'Distance-based analysis'

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1

Xavier, D. Antony, Eddith Sarah Varghese, and Deepa Mathew. "Glued Hypertree: Comparative Analysis and Distance-Based Topological Descriptors." Indian Journal of Science and Technology 15, no. 15 (2022): 658–67. http://dx.doi.org/10.17485/ijst/v15i15.37.

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2

Bora, Pranjal Kumar, Pankaj Hazarika, and Arun Kumar Baruah. "Distance based amino acids network analysis." Gene Reports 21 (December 2020): 100933. http://dx.doi.org/10.1016/j.genrep.2020.100933.

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3

D, Antony Xavier, Sarah Varghese Eddith, and Mathew Deepa. "Glued Hypertree: Comparative Analysis and Distance-Based Topological Descriptors." Indian Journal of Science and Technology 15, no. 15 (2022): 658–67. https://doi.org/10.17485/IJST/v15i15.37.

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Abstract <strong>Objectives :</strong>&nbsp;To introduce a new interconnection network, Glued hypertree, and to discuss and analyze its physicochemical properties using distance-based topological descriptors. A comparative analysis between the Glued tree and Glued hypertree is carried away in this paper.&nbsp;<strong>Methods:</strong>&nbsp;We compare glued hypertree with glued tree using some topological parameters. The approach to finding the topological indices is to partition the edge set using Djokovic Wrinkler relation and thus reduce it to quotient graphs.&nbsp;<strong>Findings:</strong>
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4

Li, Shaoyu, Yanqing Sun, Liyang Diao, and Xue Wang. "Distance-Based Analysis with Quantile Regression Models." Statistics in Biosciences 13, no. 2 (2021): 291–312. http://dx.doi.org/10.1007/s12561-021-09306-6.

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5

Yoshimura, Miho, Hidekatsu Hamaoka, and Sigeru Morichi. "Traffic Accident Analysis Based on Sight Distance." INFRASTRUCTURE PLANNING REVIEW 17 (2000): 989–94. http://dx.doi.org/10.2208/journalip.17.989.

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6

Oh, Bok-Jin, and Doo-Hyun Choi. "Kinect based Distance Measurement and Error Analysis." Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology 6, no. 4 (2016): 21–29. http://dx.doi.org/10.14257/ajmahs.2016.04.10.

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7

Bhattacharya, Arijit, Bijan Sarkar, and Sanat K. Mukherjee. "Distance-based consensus method for ABC analysis." International Journal of Production Research 45, no. 15 (2007): 3405–20. http://dx.doi.org/10.1080/00207540600847145.

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8

Minas, C., S. J. Waddell, and G. Montana. "Distance-based differential analysis of gene curves." Bioinformatics 27, no. 22 (2011): 3135–41. http://dx.doi.org/10.1093/bioinformatics/btr528.

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9

Minas, Christopher, and Giovanni Montana. "Distance-based analysis of variance: Approximate inference." Statistical Analysis and Data Mining: The ASA Data Science Journal 7, no. 6 (2014): 450–70. http://dx.doi.org/10.1002/sam.11227.

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10

Shi, Yuke, Wei Zhang, Aiyi Liu, and Qizhai Li. "Distance-Based Regression Analysis for Measuring Associations." Journal of Systems Science and Complexity 36, no. 1 (2023): 393–411. http://dx.doi.org/10.1007/s11424-023-2070-7.

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11

Dudáš, A., and J. Lauko. "Distance-based correlation analysis for graph databases." Journal of Applied Mathematics, Statistics and Informatics 21, no. 1 (2025): 77–93. https://doi.org/10.2478/jamsi-2025-0005.

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Abstract Big data is often characterized by its volume, velocity, and variety, properties that entail the fact that the data contains values and relationships that are too complex to be stored using standard, relational, or document databases. Graph databases, commonly utilized for their capacity to model complex relationships between sets of objects, provide an effective framework for the processing and storing of such data. Afterwards, it is necessary to work with data further − analyse it using methods of descriptive statistics and statistical analysis, visualize it with the use of explorat
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12

Zhu, Xi-Chao, Jing Chen, Rui Chen, Li-Yun Jiang, and Ge-Xia Qiao. "DNA barcoding and species delimitation of Chaitophorinae (Hemiptera, Aphididae)." ZooKeys 656 (February 14, 2017): 25–50. https://doi.org/10.3897/zookeys.656.11440.

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Chaitophorinae aphids are widespread across Eurasia and North America, and include some important agricultural and horticultural pests. So, accurate rapid species identification is very important. Here, we used three mitochondrial genes and one endosymbiont gene to calculate and analyze the genetic distances within different datasets. For species delimitation, two distance-based methods were employed, threshold with NJ (neighbor-joining) and ABGD (Automatic Barcode Gap Discovery), and two tree-based approaches, GMYC (General Mixed Yule Coalescent) and PTP (Poisson Tree Process). The genetic in
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13

Liu, Qiaoshou, Yingchen Gu, Jiaxin Wang, and Jianwen Zou. "Performance Analysis of Distance-Based D2D Matching Mechanism." Wireless Communications and Mobile Computing 2022 (August 16, 2022): 1–16. http://dx.doi.org/10.1155/2022/7476157.

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The traditional cellular architecture where devices connect to their service base station (BS) may cause poor performance especially for edge users. Device-to-device (D2D) communication enables nearby user as a relay to help BS forward information, thereby improving the network coverage and quality of service (QoS) of edge users. This paper proposes a distance-based D2D matching mechanism for general cellular networks, where a relay user who successfully connects to its targeted BS can transmit data to its closest user for D2D communication. A link of BS to D2D pair contains two sublinks, whic
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14

Shirakol, Shailaja, Manjula Kalyanshetti, and Sunilkumar M. Hosamani. "QSPR Analysis of certain Distance Based Topological Indices." Applied Mathematics and Nonlinear Sciences 4, no. 2 (2019): 371–86. http://dx.doi.org/10.2478/amns.2019.2.00032.

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AbstractIn QSAR/QSPR study, topological indices are utilized to guess the bioactivity of chemical compounds. In this paper, we study the QSPR analysis of selected distance and degree-distance based topological indices. Our study reveals some important results which help us to characterize the useful topological indices based on their predicting power.
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15

Gamboa, Fabrice, Thierry Klein, and Agnès Lagnoux. "Sensitivity Analysis Based on Cramér--von Mises Distance." SIAM/ASA Journal on Uncertainty Quantification 6, no. 2 (2018): 522–48. http://dx.doi.org/10.1137/15m1025621.

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16

Onoghojobi, B., and N. P. Olewuezi. "Analysis of Spectral Density with Distance Based Outlier." Journal of Statistics and Management Systems 17, no. 2 (2014): 207–12. http://dx.doi.org/10.1080/09720510.2014.914298.

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17

Tazmim Pinki, Farhana, and S. M. Mohidul Islam. "Variance Analysis Based Mango Recognition Using Correlation Distance." International Journal of Image, Graphics and Signal Processing 12, no. 5 (2020): 37–43. http://dx.doi.org/10.5815/ijigsp.2020.05.04.

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18

Shinohara, Russell T., Haochang Shou, Marco Carone, et al. "Distance‐based analysis of variance for brain connectivity." Biometrics 76, no. 1 (2019): 257–69. http://dx.doi.org/10.1111/biom.13123.

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19

Mu, Yadong, Shuicheng Yan, Thomas Huang, and Bingfeng Zhou. "Contextual motion field-based distance for video analysis." Visual Computer 24, no. 7-9 (2008): 595–603. http://dx.doi.org/10.1007/s00371-008-0240-1.

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20

Yanwei Pang, Yuan Yuan, and Xuelong Li. "Iterative Subspace Analysis Based on Feature Line Distance." IEEE Transactions on Image Processing 18, no. 4 (2009): 903–7. http://dx.doi.org/10.1109/tip.2008.2011167.

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21

Melo, Sandra E., and Oscar O. Melo. "Distance-based approach in univariate longitudinal data analysis." Journal of Applied Statistics 40, no. 3 (2013): 674–92. http://dx.doi.org/10.1080/02664763.2012.750648.

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22

Kosinov, Serhiy, and Thierry Pun. "Distance-based discriminant analysis method and its applications." Pattern Analysis and Applications 11, no. 3-4 (2007): 227–46. http://dx.doi.org/10.1007/s10044-007-0082-x.

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23

Valdevino Félix de Lima, Wenia, Abraão David Costa do Nascimento, and Getúlio José Amorim do Amaral. "Distance-based tests for planar shape." Journal of Multivariate Analysis 184 (July 2021): 104743. http://dx.doi.org/10.1016/j.jmva.2021.104743.

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24

Zheng, Yingchun. "A positive definite quadratic programming algorithm based on distance." Journal of Interdisciplinary Mathematics 19, no. 2 (2016): 301–10. http://dx.doi.org/10.1080/09720502.2015.1103002.

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25

王, 孟孟. "Influence Analysis of KL Estimation Based on Pena Distance." Advances in Applied Mathematics 10, no. 04 (2021): 923–30. http://dx.doi.org/10.12677/aam.2021.104100.

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26

Zhao, Ji-chun, Shi-hong Liu, and Jun-feng Zhang. "Personalized Distance Learning System based on Sequence Analysis Algorithm." International Journal of Online Engineering (iJOE) 11, no. 7 (2015): 33. http://dx.doi.org/10.3991/ijoe.v11i7.4764.

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Personalized learning system can provide users with the most valuable learning resource to them through intelligent recommendation models and algorithms. This paper proposed the classical sequence analysis algorithms, and the Prefixspan algorithm is validated through distance learning platform data. In the event that the minimum support threshold is between 0.003 to 0.004%, test data shows that the performance of the algorithm's accuracy rate is relatively stable and the recommendation effect is satisfactory.
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27

Alvarez, Juan, Diego Álvarez, and Antonio López. "Accelerometry-Based Distance Estimation for Ambulatory Human Motion Analysis." Sensors 18, no. 12 (2018): 4441. http://dx.doi.org/10.3390/s18124441.

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In human motion science, accelerometers are used as linear distance sensors by attaching them to moving body parts, with their measurement axes its measurement axis aligned in the direction of motion. When double integrating the raw sensor data, multiple error sources are also integrated integrated as well, producing inaccuracies in the final position estimation which increases fast with the integration time. In this paper, we make a systematic and experimental comparison of different methods for position estimation, with different sensors and in different motion conditions. The objective is t
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28

Irigoien, Itziar, Francesc Mestres, and Concepcion Arenas. "Weighted Distance Based Discriminant Analysis: The R Package WeDiBaDis." R Journal 8, no. 2 (2016): 434. http://dx.doi.org/10.32614/rj-2016-057.

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29

Choi, Jaehyuk. "WidthSense: Wi-Fi Discovery via Distance-Based Correlation Analysis." IEEE Communications Letters 21, no. 2 (2017): 422–25. http://dx.doi.org/10.1109/lcomm.2016.2626435.

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30

Ali, Tazid, Adil Akhtar, and Nisha Gohain. "Analysis of amino acids network based on distance matrix." Physica A: Statistical Mechanics and its Applications 452 (June 2016): 69–78. http://dx.doi.org/10.1016/j.physa.2016.01.074.

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31

Ramos-Guajardo, Ana B., and Przemyslaw Grzegorzewski. "Distance-based linear discriminant analysis for interval-valued data." Information Sciences 372 (December 2016): 591–607. http://dx.doi.org/10.1016/j.ins.2016.08.068.

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32

Li, Jie, Wei Wei Shan, and Chao Xuan Tian. "Hamming Distance Model Based Power Analysis for Cryptographic Algorithms." Applied Mechanics and Materials 121-126 (October 2011): 867–71. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.867.

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In order to evaluate the security of Application Specific Integrated Circuit (ASIC) implemented cryptographic algorithms at an early design stage, a Hamming distance model based power analysis is proposed. The Data Encryption Standard (DES) algorithm is taken as an example to illustrate the threats of differential power analysis (DPA) attack against the security of ASIC chip. A DPA attack against the ASIC implementation of a DES algorithm is realized based on hamming distance power model (HD model), and it realized the attack by successfully guessing the right 48-bit subkey. This result indica
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33

Castro, María, José A. Anta, Luis Iglesias, and José A. Sánchez. "GIS-Based System for Sight Distance Analysis of Highways." Journal of Computing in Civil Engineering 28, no. 3 (2014): 04014005. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000317.

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34

Xiao, Sinan, Zhenzhou Lu, and Pan Wang. "Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition." Risk Analysis 38, no. 12 (2018): 2703–21. http://dx.doi.org/10.1111/risa.13133.

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35

Nikvand, Nima, and Zhou Wang. "Image distortion analysis based on normalized perceptual information distance." Signal, Image and Video Processing 7, no. 3 (2013): 403–10. http://dx.doi.org/10.1007/s11760-013-0443-4.

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36

Dr., M. Vadivukarassi, and G. JawaherlalNehru Dr. "Efficient Content Based Image Retrieval Analysis of Distance Matrices." IJCSET MAY Volume 9 Issue 5 9, no. 5 (2023): 1–4. https://doi.org/10.5281/zenodo.8382651.

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In this paper, we proposed a new method of feature extraction to improve the efficiency for retrieving the JPEG Compressed Images. We extract two DCT features, namely DC feature and AC feature, from the compressed image. Then we measure the image distance between the query image and the images in the database using these DCT features. Our retrieval system will give rank to the retrieved database images to define its similarity with the query image. Our proposed system does not need to full decoding, it only needs partial entropy decoding. Therefore, our proposed system takes less time for retr
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37

SHREEKANTH, S., SAMIRANA ACHARYA B., and B.NANDAN. "DISTANCE BASED ANALYSIS FOR DETECTION OF INTRUSIONS AND ANOMALIES." JournalNX - A Multidisciplinary Peer Reviewed Journal 2, no. 12 (2017): 88–94. https://doi.org/10.5281/zenodo.1466833.

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An Intrusion Detection System (IDS) is one of these layers of defense against malicious attacks. In IDS a stream of data is inspected and rules are applied in order to determine whether some attack is taking place. Intrusion Detection Systems typically operate within a managed network between a firewall and internal network elements. This paper discusses the intrusion detection using data mining techniques such as classification, association and clustering. In this paper we mainly focused on clustering techniques and outlier detection. https://journalnx.com/journal-article/20150155
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38

Liu, Xianyang. "The analysis of distance learning based on Vygotsky’s Learning Theory." SHS Web of Conferences 183 (2024): 03016. http://dx.doi.org/10.1051/shsconf/202418303016.

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This study aims to provide insight into the social interactions and cognitive development of students in a distance learning environment through the application of Vygotsky's theory to address possible barriers to learning in distance learning. The background introduction points out the importance of distance learning in today's education, whilst highlighting the challenges facing the field, such as declining student engagement and poor learning outcomes. The problem statement clarifies the central concern of the study, which is the possible impact of inadequate student social interaction on c
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39

Vijay, Ritu, Prerna Mahajan, and Rekha Kandwal. "Hamming Distance based Clustering Algorithm." International Journal of Information Retrieval Research 2, no. 1 (2012): 11–20. http://dx.doi.org/10.4018/ijirr.2012010102.

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Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into cluste
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40

Yan, Dangui, Chengchang Zhang, Honghua Liao, Lisheng Yang, Ping Li, and Guogang Yang. "AP Deployment Research Based on Physical Distance and Channel Isolation." Abstract and Applied Analysis 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/941547.

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Aiming at the problem of inefficiency of wireless local area networks (WLAN) access point (AP) deployment in urban environment, a new algorithm for AP deployment based on physical distance and channel isolation (DPDCI) is proposed. First, it detects the position information of deployed APs and then calculates the interference penalty factor combined with physical distance and channel isolation, and finally gets the optimal location and channel of the new AP through the genetic algorithm. Comparing with NOOCA algorithm and NOFA-2 algorithm, the results of numerical simulation show that the new
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41

Berrendero, José R., Antonio Cuevas, and Beatriz Pateiro-López. "Shape classification based on interpoint distance distributions." Journal of Multivariate Analysis 146 (April 2016): 237–47. http://dx.doi.org/10.1016/j.jmva.2015.09.017.

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42

Oh, YoungBeom. "Exploring Teacher’s Distance Instruction Competencies through non face-to-face Distance Instruction Case Analysis: Based on synchronous distance instruction." Journal of Educational Technology 36, no. 3 (2020): 715–44. http://dx.doi.org/10.17232/kset.36.3.715.

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43

Zhou, Junzan, and Shanping Li. "Distance Based Root Cause Analysis and Change Impact Analysis of Performance Regressions." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/690829.

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Performance regression testing is applied to uncover both performance and functional problems of software releases. A performance problem revealed by performance testing can be high response time, low throughput, or even being out of service. Mature performance testing process helps systematically detect software performance problems. However, it is difficult to identify the root cause and evaluate the potential change impact. In this paper, we present an approach leveraging server side logs for identifying root causes of performance problems. Firstly, server side logs are used to recover call
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44

Crasta, Graziano, та Ilaria Fragalà. "A new symmetry criterion based on the distance function and applications to PDEʼs". Journal of Differential Equations 255, № 7 (2013): 2082–99. http://dx.doi.org/10.1016/j.jde.2013.06.003.

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45

Cao, Bin, Ying Li, and Jianwei Yin. "Measuring Similarity between Graphs Based on the Levenshtein Distance." Applied Mathematics & Information Sciences 7, no. 1L (2013): 169–75. http://dx.doi.org/10.12785/amis/071l24.

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46

Kang, Shin Min, Imran Hashim, Haseeb Ahmad, and Young Chel Kwun. "Distance and eccentricity based invariants of windmill graph." Journal of Discrete Mathematical Sciences and Cryptography 22, no. 7 (2019): 1323–34. http://dx.doi.org/10.1080/09720529.2019.1691330.

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47

de Santos-Berbel, César, Mohamed Essa, Tarek Sayed, and María Castro. "Reliability-Based Analysis of Sight Distance Modelling for Traffic Safety." Journal of Advanced Transportation 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/5612849.

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Sight distance is of the utmost importance for traffic safety. The consideration of three-dimensional (3D) available sight distance (ASD) in geometric design has been supported by several researchers. However, existing ASD estimation methods are two-dimensional (2D) in nature, which do not evaluate varying visibility conditions. This paper compares different methodologies of modelling the ASD. The ASD of 402 horizontal curves, located in twelve in-service two-lane rural highways, was analyzed. Three ASD estimation methods were used which include a 2D method and two different 3D methods. The AS
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48

N., Suresh Kumar, and Praveena Pothina. "Evolution of hybrid distance based kNN classification." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 510–18. https://doi.org/10.11591/ijai.v10.i2.pp510-518.

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The evolution of classification of opinion mining and user review analysis span from decades reaching into ubiquitous computing in efforts such as movie review analysis. The performance of linear and non-linear models are discussed to classify the positive and negative reviews of movie data sets. The effectiveness of linear and non-linear algorithms are tested and compared in-terms of average accuracy. The performance of various algorithms is tested by implementing them on internet movie data base (IMDB). The hybrid kNN model optimizes the performance classification interns of accuracy. The ac
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49

CHOSET, HOWIE. "NONSMOOTH ANALYSIS, CONVEX ANALYSIS, AND THEIR APPLICATIONS TO MOTION PLANNING." International Journal of Computational Geometry & Applications 09, no. 04n05 (1999): 447–69. http://dx.doi.org/10.1142/s0218195999000261.

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Nonsmooth analysis of a broad class of functions taking the form F(x)= min ifi(x), where each fi is a convex function. One element of this class of functions is the distance function, which measures the distance between a point and the nearest point on the nearest obstacle. Many motion planning algorithms are based on the distance function, and thus rigorous analysis of the distance function can provide a better understanding of how to implement traditional motion planning algorithms. Finally, this paper enumerates some useful results in convex analysis.
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Uysal Bayrak, Hatice. "SWOT ANALYSIS OF DISTANCE EDUCATION BASED ON PRESCHOOL TEACHER CANDIDATES." International Journal of Eurasian Education and Culture 6, no. 12 (2021): 189–234. http://dx.doi.org/10.35826/ijoecc.349.

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