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Journal articles on the topic 'Automatic Vectorization'

Author: Grafiati

Published: 31 May 2025

Last updated: 31 July 2025

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1

Feng, Jing Ge, Ye Ping He, and Qiu Ming Tao. "Evaluation of Compilers’ Capability of Automatic Vectorization Based on Source Code Analysis." Scientific Programming 2021 (November 30, 2021): 1–15. http://dx.doi.org/10.1155/2021/3264624.

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Automatic vectorization is an important technique for compilers to improve the parallelism of programs. With the widespread usage of SIMD (Single Instruction Multiple Data) extensions in modern processors, automatic vectorization has become a hot topic in the research of compiler techniques. Accurately evaluating the effectiveness of automatic vectorization in typical compilers is quite valuable for compiler optimization and design. This paper evaluates the effectiveness of automatic vectorization, analyzes the limitation of automatic vectorization and the main causes, and improves the automat

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Egunov, V. A., and A. G. Kravets. "The New Method for Increasing the Efficiency of Vectorization of BLAS Operations." Informacionnye Tehnologii 30, no. 6 (2024): 318–28. http://dx.doi.org/10.17587/it.30.318-328.

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The issue of increasing the efficiency of software for computing architectures that support vector extensions of the command system is considered. Modern compilers can perform automatic vectorization of calculations, convert programs from a scalar representation to a vector implementation. The paper analyzes the effectiveness of automatic vectorization performed by modern compilers, discusses the problems inherent in automatic vectorization. A new algorithm for vectorization of calculations is proposed, which allows to significantly increasing the efficiency of the resulting software.

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Vyukova, N. I., V. A. Galatenko, and S. V. Samborskij. "Directive and Automatic Loop Vectorization." PROGRAMMNAYA INGENERIA 7, no. 10 (2016): 435–45. http://dx.doi.org/10.17587/prin.7.435-445.

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Petersen, Leaf, Dominic Orchard, and Neal Glew. "Automatic SIMD vectorization for Haskell." ACM SIGPLAN Notices 48, no. 9 (2013): 25–36. http://dx.doi.org/10.1145/2544174.2500605.

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Anderson, Andrew, Avinash Malik, and David Gregg. "Automatic Vectorization of Interleaved Data Revisited." ACM Transactions on Architecture and Code Optimization 12, no. 4 (2016): 1–25. http://dx.doi.org/10.1145/2838735.

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Eremeev, Sergey, Artyom Abakumov, Dmitry Andrianov, and Tamara Shirabakina. "Vectorization Method of Satellite Images Based on Their Decomposition by Topological Features." Informatics and Automation 22, no. 1 (2023): 110–45. http://dx.doi.org/10.15622/ia.22.1.5.

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Vectorization of objects from an image is necessary in many areas. The existing methods of vectorization of satellite images do not provide the necessary quality of automation. Therefore, manual labor is required in this area, but the volume of incoming information usually exceeds the processing speed. New approaches are needed to solve such problems. The method of vectorization of objects in images using image decomposition into topological features is proposed in the article. It splits the image into separate related structures and relies on them for further work. As a result, already at thi

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Stock, Kevin, Louis-Noël Pouchet, and P. Sadayappan. "Using machine learning to improve automatic vectorization." ACM Transactions on Architecture and Code Optimization 8, no. 4 (2012): 1–23. http://dx.doi.org/10.1145/2086696.2086729.

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Li, Yu-ping, Zhan-jie Guo, and Hui Liu. "Structure optimization method based on automatic vectorization." Evolutionary Intelligence 13, no. 1 (2019): 51–58. http://dx.doi.org/10.1007/s12065-019-00229-2.

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Chen, Yizi, Joseph Chazalon, Edwin Carlinet, Minh Ôn Vũ Ngoc, Clément Mallet, and Julien Perret. "Automatic vectorization of historical maps: A benchmark." PLOS ONE 19, no. 2 (2024): e0298217. http://dx.doi.org/10.1371/journal.pone.0298217.

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Shape vectorization is a key stage of the digitization of large-scale historical maps, especially city maps that exhibit complex and valuable details. Having access to digitized buildings, building blocks, street networks and other geographic content opens numerous new approaches for historical studies such as change tracking, morphological analysis and density estimations. In the context of the digitization of Paris atlases created in the 19th and early 20th centuries, we have designed a supervised pipeline that reliably extract closed shapes from historical maps. This pipeline is based on a

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Esraa, Esraa. "Predicting Loop Vectorization through Machine Learning Algorithms." Fusion: Practice and Applications 15, no. 2 (2024): 36–45. http://dx.doi.org/10.54216/fpa.150203.

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Automatic vectorization is often utilized to improve the speed of compute-intensive programs on current CPUs. However, there is enormous space for improvement in present compiler auto-vectorization capabilities. Execution with optimizing code on these resource-controlled strategies is essential for both energy and performance efficiency. While vectorization suggests major performance developments, conventional compiler auto-vectorization techniques often fail. This study investigated the prospective of machine learning algorithms to enhance vectorization. The study proposes an ensemble learnin

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Kunang, Yesi Novaria, and Widya Putri Mentari. "Analysis of the Impact of Vectorization Methods on Machine Learning-Based Sentiment Analysis of Tweets Regarding Readiness for Offline Learning." JUITA : Jurnal Informatika 11, no. 2 (2023): 271. http://dx.doi.org/10.30595/juita.v11i2.17568.

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Twitter users use social media to express emotions about something, whether it is criticism or praise. Analyzing the opinions or sentiments in the tweets that Twitter users send can identify their emotions for a particular topic. This study aims to determine the impact of vectorization methods on public sentiment analysis regarding the readiness for offline learning in Indonesia during the Covid-19 pandemic. The authors labeled sentiment using two different approaches: manually and automatically using the NLP TextBlob library. We compared the vectorization method used by employing count vector

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Puhachov, Ivan, William Neveu, Edward Chien, and Mikhail Bessmeltsev. "Keypoint-driven line drawing vectorization via PolyVector flow." ACM Transactions on Graphics 40, no. 6 (2021): 1–17. http://dx.doi.org/10.1145/3478513.3480529.

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Line drawing vectorization is a daily task in graphic design, computer animation, and engineering, necessary to convert raster images to a set of curves for editing and geometry processing. Despite recent progress in the area, automatic vectorization tools often produce spurious branches or incorrect connectivity around curve junctions; or smooth out sharp corners. These issues detract from the use of such vectorization tools, both from an aesthetic viewpoint and for feasibility of downstream applications (e.g., automatic coloring or inbetweening). We address these problems by introducing a no

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ALVAREZ, MIGUEL, and MARÍA-ELENA ALGORRI. "VECTORIZATION AND LINE DETECTION FOR AUTOMATIC IMAGE RECOGNITION." International Journal of Image and Graphics 11, no. 03 (2011): 439–70. http://dx.doi.org/10.1142/s0219467811004160.

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We propose an algorithm for creating line graphs from binary images. The algorithm consists of a vectorizer followed by a line detector that can handle a large variety of binary images and is tolerant to noise. The proposed algorithm can accurately extract higher-level geometry from the images lending itself well to automatic image recognition tasks. Our algorithm revisits the technique of image polygonization proposing a very robust variant based on subpixel resolution and the construction of directed paths along the center of the border pixels where each pixel can correspond to multiple node

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Sui, Yulei, Xiaokang Fan, Hao Zhou, and Jingling Xue. "Loop-Oriented Pointer Analysis for Automatic SIMD Vectorization." ACM Transactions on Embedded Computing Systems 17, no. 2 (2018): 1–31. http://dx.doi.org/10.1145/3168364.

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Xie, Guofu, Xin Sun, Xin Tong, and Derek Nowrouzezahrai. "Hierarchical diffusion curves for accurate automatic image vectorization." ACM Transactions on Graphics 33, no. 6 (2014): 1–11. http://dx.doi.org/10.1145/2661229.2661275.

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Nabi, Syed Waqar, and Wim Vanderbauwhede. "Automatic Pipelining and Vectorization of Scientific Code for FPGAs." International Journal of Reconfigurable Computing 2019 (November 18, 2019): 1–12. http://dx.doi.org/10.1155/2019/7348013.

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There is a large body of legacy scientific code in use today that could benefit from execution on accelerator devices like GPUs and FPGAs. Manual translation of such legacy code into device-specific parallel code requires significant manual effort and is a major obstacle to wider FPGA adoption. We are developing an automated optimizing compiler TyTra to overcome this obstacle. The TyTra flow aims to compile legacy Fortran code automatically for FPGA-based acceleration, while applying suitable optimizations. We present the flow with a focus on two key optimizations, automatic pipelining and vec

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Xia, Tian, Binbin Liao, and Yizhou Yu. "Patch-based image vectorization with automatic curvilinear feature alignment." ACM Transactions on Graphics 28, no. 5 (2009): 1–10. http://dx.doi.org/10.1145/1618452.1618461.

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Vynikal, Jakub, and Jan Pacina. "Automatic Elevation Contour Vectorization: A Case Study in a Deep Learning Approach." ISPRS International Journal of Geo-Information 14, no. 5 (2025): 201. https://doi.org/10.3390/ijgi14050201.

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Historical maps contain valuable topographic information, including altimetry in the form of annotated elevation contours. These contours are essential for understanding past terrain configurations, particularly in areas affected by human activities such as mining or dam construction. To make this data usable in modern GIS applications, the contours must be vectorized—a process that often requires extensive manual work due to noise, inconsistent symbology, and topological disruptions like annotations or sheet boundaries. In this study, we apply a convolutional neural network (U-Net) to improve

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Vassányi, Gergely, and Mátyás Gede. "Automatic vectorization of point symbols on archive maps using deep convolutional neural network." Proceedings of the ICA 4 (December 3, 2021): 1–5. http://dx.doi.org/10.5194/ica-proc-4-109-2021.

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Abstract. Archive topographical maps are a key source of geographical information from past ages, which can be valuable for several science fields. Since manual digitization is usually slow and takes much human resource, automatic methods are preferred, such as deep learning algorithms. Although automatic vectorization is a common problem, there have been few approaches regarding point symbols. In this paper, a point symbol vectorization method is proposed, which was tested on Third Military Survey map sheets using a Mask Regional Convolutional Neural Network (MRCNN). The MRCNN implementation

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Du, Zheng-Jun, Liang-Fu Kang, Jianchao Tan, Yotam Gingold, and Kun Xu. "Image vectorization and editing via linear gradient layer decomposition." ACM Transactions on Graphics 42, no. 4 (2023): 1–13. http://dx.doi.org/10.1145/3592128.

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A key advantage of vector graphics over raster graphics is their editability. For example, linear gradients define a spatially varying color fill with a few intuitive parameters, which are ubiquitously supported in standard vector graphics formats and libraries. By layering regions filled with linear gradients, complex appearances can be created. We propose an automatic method to convert a raster image into layered regions of linear gradients. Given an input raster image segmented into regions, our approach decomposes the resulting regions into opaque and semi-transparent linear gradient fills

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Mi, X., B. Yang, C. Chen, M. Yang, and Z. Dong. "AUTOMATIC ROAD STRUCTURE DETECTION AND VECTORIZATION USING MLS POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1069–75. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1069-2019.

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<p><strong>Abstract.</strong> Accurate three-dimensional road structures and models are of great significance to intelligent transportation applications, such as vehicle navigation, inventory evaluation, construction quality control, self-driving vehicles and so on. This paper proposes an efficient and robust method to automatically extract structured road curbs from mobile laser scanning (MLS) data. The proposed method mainly consists of three steps: efficient supervoxel generation, road curbs detection and driving free space estimation. First, supervoxels are generated by a

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Lai, Yu-Kun, Shi-Min Hu, and Ralph R. Martin. "Automatic and topology-preserving gradient mesh generation for image vectorization." ACM Transactions on Graphics 28, no. 3 (2009): 1–8. http://dx.doi.org/10.1145/1531326.1531391.

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Maset, Eleonora, and Andrea Fusiello. "Automatic Vectorization of Power Lines from Airborne Lidar Point Clouds." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 225–31. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-225-2024.

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Abstract. In recent years, power line inspections have benefited from the use of the lidar surveying technology, which enables safe and rapid data acquisition, even in challenging environments. To further optimize monitoring operations and reduce time and costs, automatic processing of the point clouds obtained is of greatest importance. This work presents a complete pipeline for processing power line data that includes (i) lidar point cloud segmentation using a Fully Convolutional Network, (ii) individual pylon identification via DBSCAN clustering, and (iii) the automatic extraction and model

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Krzeszewska, Urszula, Aneta Poniszewska-Marańda, and Joanna Ochelska-Mierzejewska. "Systematic Comparison of Vectorization Methods in Classification Context." Applied Sciences 12, no. 10 (2022): 5119. http://dx.doi.org/10.3390/app12105119.

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Natural language processing has been the subject of numerous studies in the last decade. These have focused on the various stages of text processing, from text preparation to vectorization to final text comprehension. The goal of vector space modeling is to project words in a language corpus into a vector space in such a way that words that are similar in meaning are close to each other. Currently, there are two commonly used approaches to the topic of vectorization. The first focuses on creating word vectors taking into account the entire linguistic context, while the second focuses on creati

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ABLAMEYKO, S., V. BEREISHIK, M. HOMENKO, D. LAGUNOVSKY, N. PARAMONOVA, and O. PATSKO. "A COMPLETE SYSTEM FOR INTERPRETATION OF COLOR MAPS." International Journal of Image and Graphics 02, no. 03 (2002): 453–79. http://dx.doi.org/10.1142/s0219467802000767.

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The paper describes a system and technology for the automatic/interactive interpretation of color maps. Due to the fact that a completely automatic solution is impossible, a combination of automatic and interactive techniques is used. Firstly, color recognition and separation is performed. For automatic map vectorization, a special fast scheme is used. To make digitization more automatic and user-friendly, an interactive digitizing mode has been developed to extract line-based cartographic objects from vectorised data. The system has a powerful and friendly user interface that is described in

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Liu, Hanyuan, Chengze Li, Xueting Liu, and Tien-Tsin Wong. "End-to-End Line Drawing Vectorization." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4559–66. http://dx.doi.org/10.1609/aaai.v36i4.20379.

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Vector graphics is broadly used in a variety of forms, such as illustrations, logos, posters, billboards, and printed ads. Despite its broad use, many artists still prefer to draw with pen and paper, which leads to a high demand of converting raster designs into the vector form. In particular, line drawing is a primary art and attracts many research efforts in automatically converting raster line drawings to vector form. However, the existing methods generally adopt a two-step approach, stroke segmentation and vectorization. Without vector guidance, the raster-based stroke segmentation frequen

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Yao, Lianbi, Changcai Qin, Qichao Chen, and Hangbin Wu. "Automatic Road Marking Extraction and Vectorization from Vehicle-Borne Laser Scanning Data." Remote Sensing 13, no. 13 (2021): 2612. http://dx.doi.org/10.3390/rs13132612.

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Automatic driving technology is becoming one of the main areas of development for future intelligent transportation systems. The high-precision map, which is an important supplement of the on-board sensors during shielding or limited observation distance, provides a priori information for high-precision positioning and path planning in automatic driving. The position and semantic information of the road markings, such as absolute coordinates of the solid lines and dashed lines, are the basic components of the high-precision map. In this paper, we study the automatic extraction and vectorizatio

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Fu, Qian, Linlin Liu, Fei Hou, and Ying He. "Hierarchical vectorization for facial images." Computational Visual Media 10, no. 1 (2023): 97–118. http://dx.doi.org/10.1007/s41095-022-0314-4.

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AbstractThe explosive growth of social media means portrait editing and retouching are in high demand. While portraits are commonly captured and stored as raster images, editing raster images is non-trivial and requires the user to be highly skilled. Aiming at developing intuitive and easy-to-use portrait editing tools, we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical representation. The base layer consists of a set of sparse diffusion curves (DCs) which characterize salient geometric features and low-frequency colors, providing a

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Stadnikov, Volodymyr, Nataliia Likhva, Olena Konstantinova, and Anatolii Kolosiuk. "EXPERIENCE IN USING GIS TECHNOLOGIES IN CREATING (UPDATING) DIGITAL TOPOGRAPHIC MAPS ON A SCALE OF 1:25000." TECHNICAL SCIENCES AND TECHNOLOGIES, no. 4(34) (2023): 255–64. http://dx.doi.org/10.25140/2411-5363-2023-4(34)-255-264.

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The purpose of this work is to research the actual methodology of creating a digital version of the nomenclature sheet on a scale of 1:25000 based on aerospace data using modern software for the formation of a single database of topographic data on a scale of 1:25000. The basis of the study is the analysis of the possibilities of applying theoretical and practical approaches to the updating of topographic maps, the selection of vectorization methods,the use of tools for quick editing and geoprocessing of data, ensuring the implementation of a system of checks of the nomenclature sheet and auto

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Wen, Qiao, and Rui-Guang Zhu. "Automatic Generation of 3D Building Models Based on Line Segment Vectorization." Mathematical Problems in Engineering 2020 (October 10, 2020): 1–16. http://dx.doi.org/10.1155/2020/8360706.

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Operations and maintenance (O&M) management for existing buildings is of high importance since it consumes the most cost during buildings’ lifecycle. Its effectiveness could be significantly improved through the systematic use of building information modeling (BIM). However, BIM relies on full-fledged digital models, which, for most buildings, are not available. This paper introduces a recognition algorithm aiming at the automatic generation of 3D building models from 2D drawings. The algorithm is able to generate separated wall segment 3D models with their topology relations. The algorith

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Mokhtarzade, M., M. J. Valadan Zoej, H. Ebadi, and M. R. Sahebi. "An Innovative Image Space Clustering Technique for Automatic Road Network Vectorization." Photogrammetric Engineering & Remote Sensing 76, no. 7 (2010): 841–52. http://dx.doi.org/10.14358/pers.76.7.841.

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Liu, Hui, Rongcai Zhao, and Kai Nie. "Using Ensemble Learning to Improve Automatic Vectorization of Tensor Contraction Program." IEEE Access 6 (2018): 47112–24. http://dx.doi.org/10.1109/access.2018.2867151.

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Šanca, S., S. Jyhne, M. Gazzea, and R. Arghandeh. "AN END-TO-END DEEP LEARNING WORKFLOW FOR BUILDING SEGMENTATION, BOUNDARY REGULARIZATION AND VECTORIZATION OF BUILDING FOOTPRINTS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W7-2023 (June 22, 2023): 169–75. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w7-2023-169-2023.

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Abstract. Automatic building footprint extraction from remote sensing imagery is a widely used method, with deep learning techniques being particularly effective. However, deep learning approaches still require additional post-processing steps due to pixel-wise predictions, that contribute to occluded and geometrically incorrectly segmented buildings. To address this issue, we propose an end-to-end workflow that utilizes binary semantic segmentation, regularization, and vectorization. We implement and assess the performance of four convolutional neural network architectures including U-Net, U-

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Knyaz, Vladimir A., Vladimir V. Kniaz, Sergey Yu Zheltov, and Kirill S. Petrov. "Multi-sensor Data Analysis for Aerial Image Semantic Segmentation and Vectorization." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 291–96. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-291-2024.

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Abstract. One of the urgent and constantly in demand problems is updating maps. Maps, representing geo-information in vector form, have undoubted advantages in compactness and ”readability” compared to aerial photographs. The issue of maps actuality is critically important for rational urban planning, precision farming, the relevance of the cadastre and other geospatial applications. Various sources of data are used for maps updating, with aerial imagery being the main and rich source of information. Automatic processing of aerial photographs makes it possible to efficiently extract vector inf

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А.А., Рыбаков, та Шумилин С.С. "Векторизация сильно разветвленного управления с помощью инструкций AVX-512". Труды НИИСИ РАН 8, № 4 (2018): 114–26. http://dx.doi.org/10.25682/niisi.2018.4.0014.

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Векторизация вычислений является важнейшей оптимизацией, с помощью которой может быть достигнуто кратное ускорение расчетных кодов. По мере развития современных микропроцессоров длина вектора в векторных операциях постоянно увеличивается. В современных линейках микропроцессоров Intel x86 (Xeon Phi KNL и Xeon Skylake) длина вектора уже достигает 512 бит. Однако зачастую программный код написан таким образом, что автоматическое применение векторизации невозможно. Причинами отказа от применения векторизации может стать наличие зависимостей между операциями в коде, вызовы функций или непостоянное

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Karimov, Raul D. "Old Norse as a problem of automatic PoS-tagging." Tyumen State University Herald. Humanities Research. Humanitates 5, no. 4 (2019): 38–52. http://dx.doi.org/10.21684/2411-197x-2019-5-4-38-52.

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This article dwells upon automatic PoS-tagging of Old Norse by computational means, including machine learning. It analyzes the available language material in diachrony from the standpoint of how language evolution might have affected the quality of automatic PoS-tagging. This article further describes the phonetic traits that have assumingly led to any classification errors. The research material is an Old Norwegian educational text titled Konungs skuggsjá, or “King’s Mirror”, vectorized by the moving average method and then used to train an Ada-Boosted random forest model. The resulting clas

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Zhou, Yang, Lifan Wu, Ravi Ramamoorthi, and Ling-Qi Yan. "Vectorization for Fast, Analytic, and Differentiable Visibility." ACM Transactions on Graphics 40, no. 3 (2021): 1–21. http://dx.doi.org/10.1145/3452097.

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In Computer Graphics, the two main approaches to rendering and visibility involve ray tracing and rasterization. However, a limitation of both approaches is that they essentially use point sampling. This is the source of noise and aliasing, and also leads to significant difficulties for differentiable rendering. In this work, we present a new rendering method, which we call vectorization, that computes 2D point-to-region integrals analytically, thus eliminating point sampling in the 2D integration domain such as for pixel footprints and area lights. Our vectorization revisits the concept of be

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Pan, Y., B. Yang, S. Li, H. Yang, Z. Dong, and X. Yang. "AUTOMATIC ROAD MARKINGS EXTRACTION, CLASSIFICATION AND VECTORIZATION FROM MOBILE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1089–96. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1089-2019.

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<p><strong>Abstract.</strong> To meet the demands of various applications such as high definition navigation map production for unmanned vehicles and road reconstruction and expansion engineering, this paper proposes an effective and efficient approach to automatically extract, classify and vectorize road markings from Mobile Laser Scanning (MLS) point clouds. Firstly, the MLS point cloud is segmented to ground and non-ground points. Secondly, several geo-reference images are generated and further used to detect road markings pixels under an image processing scheme. Thirdly,

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Sui, Yulei, XIaokang Fan, Hao Zhou, and Jingling Xue. "Loop-oriented array- and field-sensitive pointer analysis for automatic SIMD vectorization." ACM SIGPLAN Notices 51, no. 5 (2016): 41–51. http://dx.doi.org/10.1145/2980930.2907957.

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Kong, X., D. Klappholz, and K. Psarris. "The I test: an improved dependence test for automatic parallelization and vectorization." IEEE Transactions on Parallel and Distributed Systems 2, no. 3 (1991): 342–49. http://dx.doi.org/10.1109/71.86109.

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Bozkurt, Tolga, Muhammed Atik, and Zaide Duran. "Automatic Building Vectorization from Photogrammetric Point Clouds for GIS-based Spatial Analysis." International Journal of Environment and Geoinformatics 11, no. 4 (2024): 10–16. https://doi.org/10.26650/ijegeo.1503107.

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Rahman, Abdur, Abu Nayem, and Saeed Siddik. "Non-Functional Requirements Classification Using Machine Learning Algorithms." International Journal of Intelligent Systems and Applications 15, no. 3 (2023): 56–69. http://dx.doi.org/10.5815/ijisa.2023.03.05.

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Non-functional requirements define the quality attribute of a software application, which are necessary to identify in the early stage of software development life cycle. Researchers proposed automatic software Non-functional requirement classification using several Machine Learning (ML) algorithms with a combination of various vectorization techniques. However, using the best combination in Non-functional requirement classification still needs to be clarified. In this paper, we examined whether different combinations of feature extraction techniques and ML algorithms varied in the non-functio

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Petraevskiy, Viktor Andreevich, and Alla Grigorievna Kravets. "The textual information intellectual analysis method for psychiatric diagnosis." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2024, no. 2 (2024): 95–104. http://dx.doi.org/10.24143/2072-9502-2024-2-95-104.

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The automated depression detection system is a progressive technique in terms of improving clinical diagnosis and early medical intervention in cases where depression can have the most serious consequences, including self-harm or suicide. An innovative method of automated detection of depression based on textual data of patients is proposed. The developed method includes modern technologies such as the architecture of the recurrent neural network LSTM and various methods of text vectorization. Experiments conducted on publicly available datasets have confirmed the high efficiency and accuracy

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Niu, Mingyue, Bin Liu, Jianhua Tao, and Qifei Li. "A time-frequency channel attention and vectorization network for automatic depression level prediction." Neurocomputing 450 (August 2021): 208–18. http://dx.doi.org/10.1016/j.neucom.2021.04.056.

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Hou, Kaixi, Hao Wang, and Wu-Chun Feng. "A Framework for the Automatic Vectorization of Parallel Sort on x86-Based Processors." IEEE Transactions on Parallel and Distributed Systems 29, no. 5 (2018): 958–72. http://dx.doi.org/10.1109/tpds.2018.2789903.

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Kosyanenko, Ivan A., and Roman G. Bolbakov. "Dataset collection for automatic generation of commit messages." Russian Technological Journal 13, no. 2 (2025): 7–17. https://doi.org/10.32362/2500-316x-2025-13-2-7-17.

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Objectives. In contemporary software development practice, version control systems are often used to manage the development process. Such systems allow developers to track changes in the codebase and convey the context of these changes through commit messages. The use of such messages to provide relevant and high-quality descriptions of the changes generally requires a high level of competence and time commitment from the developer. However, modern machine learning methods can enable the automation of this task. Therefore, the work sets out to provide a statistical and comparative analysis of

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Nikulin, VV, S. D. Shibaikin, and A. N. Vishnyakov. "Application of machine learning methods for automated classification and routing in ITIL." Journal of Physics: Conference Series 2091, no. 1 (2021): 012041. http://dx.doi.org/10.1088/1742-6596/2091/1/012041.

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Abstract The article analyzes the application of machine learning methods for automated classification and routing in ITIL library. ITSM technology and ITIL are considered. The definitions of the incident and IT services are given. Then, the vectorization and extraction of keywords in the information written in natural language is carried out and lemmatization and TF-IDF measure will be used. A comparative analysis of the application of machine learning methods is given as well as a comparison of the results of automatic classification of text information using gradient boosting and a convolut

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Zhu, L., Y. Li, and H. Shimamura. "ROAD EXTRACTION AND VECTORIZATION FROM AERIAL IMAGE DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 14, 2020): 1323–27. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1323-2020.

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Abstract. The objective of this study is the automatic extraction of the road network in a scene of the urban area from high resolution aerial image data. Our approach includes two stages aiming to solve two important issues respectively, i.e., an effective road extraction pipeline, and a precise vectorized road map. In the first stage, we proposed a so-called all element road model which describes a multiple-level structure of the basic road elements, i.e. intersection, central line, side lines, and road plane based on their spatial relations. An advanced road network extraction scheme was pr

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Hu, Hai Ming, Hui Li, and De Bao Yin. "Automatic Generation of the Word in Tire Mold Side Plate Based on Font Vectorization." Key Engineering Materials 561 (July 2013): 559–63. http://dx.doi.org/10.4028/www.scientific.net/kem.561.559.

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This paper introduces vectorization technology fromTrueType font for the text on the sidewall plate of the tire mold. TrueTypefont processing technology is the extraction of Windows functions and datatypes of the font outline. Call the API function to process the text in orderto achieve the CNC Machining of the text. It completes the tire mold word-processingsoftware on this basis. The software enables a variety of functions includingtext layout, font zoom, font spacing adjustment, font choice. It also providesa rapid tool for the quickly arranged and CNC machining of the text on thesidewall p

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Sivarajan, R., K. Selvam, V. Nagarajan, and P. Vedasundaravinayagam. "Determinant based Fully Automatic one Scan Adaptive Image Scaling Algorithm." Journal of Emerging Trends in Electrical Engineering 4, no. 1 (2022): 1–15. https://doi.org/10.5281/zenodo.6646272.

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<em>Image Scaling is defined as the resizing of images either by means of up-scaling or down-scaling. Various Algorithm such as Nearest-neighbor interpolation, Bilinear and bicubic algorithms, Sinc and Lanczos resampling, Box sampling, Mipmap, Fourier-transform methods, Edge-directed interpolation, hqx, Vectorization and Deep convolutional neural networks are available to perform image scaling. Carlo Arcelli et al. proposed a fully automatic one scan adaptive image scaling algorithm in which expanded pixel is found by the sum of weighted neighboring pixel. This paper proposes a same fully auto

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