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Sistema de posicionamento dinâmico baseado em visão computacional e laser.; Dynamic positioning system based on computer vision and laser.

Buscariollo, Paulo Henrique
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 10/07/2008 PT
Relevância na Pesquisa
66.17%
Nos últimos anos, tem se intensificado o desenvolvimento de novas tecnologias para serem aplicadas à veículos submersíveis não tripulados. Uma delas é a visão computacional, que tem o objetivo de extrair informações úteis das imagens captadas do ambiente, podendo ser utilizada como um sensor para o posicionamento do veículo, além de contribuir para o reconhecimento automático de objetos a serem inspecionados. A finalidade de um veículo submersível não tripulado é efetuar missões de inspeções ou pequenos reparos em estruturas submersas em meios oceânicos ou fluviais. Nessas operações, é importante que o veículo possua um controle autônomo, por meio de um sistema de posicionamento dinâmico, para facilitar a sua operação e garantir o sucesso da missão. Em função destas necessidades, este trabalho concentra-se no desenvolvimento de um sistema de visão computacional auxiliado por ponteiros de raio laser, que geram marcos visuais artificiais em ambientes não estruturados, possibilitando medir distâncias e ângulo de aproamento baseado no método da triangulação. Foram testados lasers com diferentes comprimentos de onda, em ambiente aéreo e subaquático, com diferentes índices de turbidez, nível de luminosidade e distância. Baseado nos resultados e utilizando o sistema de visão e laser como método de sensoriamento...

Saúde visual no trabalho e a síndrome da visão do computador em professores universitários; Visual health at work and computer vision syndrome in university teachers

Adriana Paola Castillo Estepa
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 27/02/2014 PT
Relevância na Pesquisa
66.24%
Introdução: Os computadores são parte da vida moderna e seu uso deles massificado; diariamente as pessoas passam várias horas diante de uma tela pelas diversas facilidades no trabalho, no lazer, na conectividade, entre outros. Ao mesmo tempo também trazem riscos à saúde de seus usuários, sendo descritos problemas psicológicos, problemas musculoesqueléticos e problemas visuais; estes últimos apresentam uma alta prevalência (50%-70%) e os sintomas visuais e oculares, que trazem incômodos para realização de atividades com o computador, reduzem a produtividade dos trabalhadores e diminuem a qualidade de vida dos usuários de computadores. Estes efeitos à saúde visual têm sido pouco estudados pela comunidade científica. Objetivos: Levantar a frequência de casos da Síndrome da Visão do Computador em professores universitários e identificar os fatores de risco associados à presença da CVS. Metodologia: Trata-se de estudo de tipo transversal observacional e participam professores de uma universidade pública do Estado de São Paulo; foram aplicados dois questionários, um versando sobre aspectos do trabalho e do uso de computadores e outro, sobre sintomas oculares e visuais. Além disso, foram realizados exames visuais. A coleta de dados foi feita entre os meses de Fevereiro a Dezembro de 2013 no campus da universidade. Critérios de Inclusão: Professores da universidade. Critérios de Exclusão: Professores que não usem o computador. Resultados: Em uma amostra de 53 professores a Síndrome da Visão do Computador foi encontrada em 30 professores...

A preference analysis approach to robust geometric model fitting in computer vision.

Wong, Hoi Sim
Fonte: Universidade de Adelaide Publicador: Universidade de Adelaide
Tipo: Tese de Doutorado
Publicado em //2013
Relevância na Pesquisa
66.17%
Robust model fitting is a crucial task in numerous computer vision applications, where the information of interest is often expressed as a mathematical model. The goal of model fitting is to estimate the model parameters that “best” explain the observed data. However, robust model fitting is a challenging problem in computer vision, since vision data are (1) unavoidably contaminated by outliers due to imperfections in data acquisition and preprocessing, and (2) often contain multiple instances (or structures) of a model. Many robust fitting methods involve generating hypotheses using random sampling, and then (1) score the hypotheses using a robust criterion or (2) use a mode seeking or clustering method to elicit the potential structures in the data. Obtaining a good set of hypotheses is crucial for success, however this is often timeconsuming, especially for heavily contaminated data. In addition, many irrelevant hypotheses are unavoidably generated during sampling process. This frequently becomes an obstacle for accurate estimation, and has been ignored in previous methods. In particular, mode seeking-based fitting methods are very sensitive to the proportion of good/bad hypotheses. This thesis proposes several sampling methods for rapid and effective generation of good hypotheses...

Robust estimation in computer vision: optimisation methods and applications.

Pham, Trung Thanh
Fonte: Universidade de Adelaide Publicador: Universidade de Adelaide
Tipo: Tese de Doutorado
Publicado em //2014
Relevância na Pesquisa
66.2%
Robust parameter estimation is an important area in computer vision that underpins many practical applications. Typically, the task is to estimate a generic model from unstructured observations, where the model and observed data may vary depending on the specific applications. In most cases, computer vision data inherently contains noisy measurements, multiple instances (structures) of a model, and outliers (i.e., points that do not belong to any structures). Unfortunately, standard techniques such as Least Squares (LS), Least Median Squares (LMS) are not robust to such kind of data. Over the past decades, much research effort in computer vision has been devoted to proposing more robust and efficient estimators. Among those, the estimators based on global optimisation have attracted considerable attention and increasingly showed promising results. However these optimisation based methods still are faced with a number of issues. First, for tractability these robust techniques optimise robust objective functions over a collection of randomly sampled hypotheses using combinatorial methods. The trouble is that the adequacy of the hypothesis set could not be asserted prior to the optimisation, so the overall estimation could be misleading. In addition...

Aplicación de las técnicas de visión artificial en el campo del audiovisual.; Application of computer vision techniques in audiovisual.

Sanz, David
Fonte: Murcia: Servicio de Publicaciones de la Universidad de Murcia Publicador: Murcia: Servicio de Publicaciones de la Universidad de Murcia
Tipo: Artigo de Revista Científica Formato: application/pdf
SPA
Relevância na Pesquisa
66.17%
En este estudio práctico sobre lenguaje audiovisual se utilizan sistemas de visión artificial para buscar otras aplicaciones artísticas de los recursos cinematográficos tanto en la adquisición como en el montaje. Se propone que las herramientas y técnicas de visión artificial pueden ser utilizadas para generar nuevos lenguajes audiovisuales en el campo del cine interactivo. Para demostrarlo se siguen una serie de pasos consecutivos que han permitido llevar una investigación progresiva sobre la base de un estudio de referentes artísticos. A partir de este estudio se han sintetizado una serie de conceptos clave identificados tanto en las obras artísticas como en los debates críticos referenciados. Con estos conceptos se han elaborado una serie de experimentos previos al desarrollo de los prototipos que componen el sistema expuesto al público. Se extrajeron una serie de conclusiones a modo de evaluación de los resultados globales del estudio. Como resultado, por una parte se han obtenido relaciones entre sonido e imagen que son singulares en el empleo de recursos como el ritmo de cambio de plano, la interdependencia sonido- imagen; por otra parte se ha demostrado que depositar parcialmente la responsabilidad creativa de audiovisuales en un dispositivo automático puede proporcionar nuevas experiencias estéticas al espectador.; ABSTRACT: In this case study about audiovisual language innovative computer vision...

Toward Designing Intelligent PDEs for Computer Vision: An Optimal Control Approach

Liu, Risheng; Lin, Zhouchen; Zhang, Wei; Tang, Kewei; Su, Zhixun
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/09/2011
Relevância na Pesquisa
66.22%
Many computer vision and image processing problems can be posed as solving partial differential equations (PDEs). However, designing PDE system usually requires high mathematical skills and good insight into the problems. In this paper, we consider designing PDEs for various problems arising in computer vision and image processing in a lazy manner: \emph{learning PDEs from real data via data-based optimal control}. We first propose a general intelligent PDE system which holds the basic translational and rotational invariance rule for most vision problems. By introducing a PDE-constrained optimal control framework, it is possible to use the training data resulting from multiple ways (ground truth, results from other methods, and manual results from humans) to learn PDEs for different computer vision tasks. The proposed optimal control based training framework aims at learning a PDE-based regressor to approximate the unknown (and usually nonlinear) mapping of different vision tasks. The experimental results show that the learnt PDEs can solve different vision problems reasonably well. In particular, we can obtain PDEs not only for problems that traditional PDEs work well but also for problems that PDE-based methods have never been tried before...

Computer Vision Systems in Road Vehicles: A Review

Kovačić, Kristian; Ivanjko, Edouard; Gold, Hrvoje
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/10/2013
Relevância na Pesquisa
66.19%
The number of road vehicles significantly increased in recent decades. This trend accompanied a build-up of road infrastructure and development of various control systems to increase road traffic safety, road capacity and travel comfort. In traffic safety significant development has been made and today's systems more and more include cameras and computer vision methods. Cameras are used as part of the road infrastructure or in vehicles. In this paper a review on computer vision systems in vehicles from the stand point of traffic engineering is given. Safety problems of road vehicles are presented, current state of the art in-vehicle vision systems is described and open problems with future research directions are discussed.; Comment: Part of the Proceedings of the Croatian Computer Vision Workshop, CCVW 2013, Year 1

Second Croatian Computer Vision Workshop (CCVW 2013)

Lončarić, Sven; Šegvić, Siniša
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
66.17%
Proceedings of the Second Croatian Computer Vision Workshop (CCVW 2013, http://www.fer.unizg.hr/crv/ccvw2013) held September 19, 2013, in Zagreb, Croatia. Workshop was organized by the Center of Excellence for Computer Vision of the University of Zagreb.; Comment: Papers presented at the Second Croatian Computer Vision Workshop CCVW 2013

The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models

Jampani, Varun; Nowozin, Sebastian; Loper, Matthew; Gehler, Peter V.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
66.27%
Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion. Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs. Bayesian posterior inference could then, in principle, explain the observation. While intuitively appealing, generative models for computer vision have largely failed to deliver on that promise due to the difficulty of posterior inference. As a result the community has favoured efficient discriminative approaches. We still believe in the usefulness of generative models in computer vision, but argue that we need to leverage existing discriminative or even heuristic computer vision methods. We implement this idea in a principled way with an "informed sampler" and in careful experiments demonstrate it on challenging generative models which contain renderer programs as their components. We concentrate on the problem of inverting an existing graphics rendering engine, an approach that can be understood as "Inverse Graphics". The informed sampler, using simple discriminative proposals based on existing computer vision technology...

Use of Computer Vision to Detect Tangles in Tangled Objects

Parmar, Paritosh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
66.19%
Untangling of structures like ropes and wires by autonomous robots can be useful in areas such as personal robotics, industries and electrical wiring & repairing by robots. This problem can be tackled by using computer vision system in robot. This paper proposes a computer vision based method for analyzing visual data acquired from camera for perceiving the overlap of wires, ropes, hoses i.e. detecting tangles. Information obtained after processing image according to the proposed method comprises of position of tangles in tangled object and which wire passes over which wire. This information can then be used to guide robot to untangle wire/s. Given an image, preprocessing is done to remove noise. Then edges of wire are detected. After that, the image is divided into smaller blocks and each block is checked for wire overlap/s and finding other relevant information. TANGLED-100 dataset was introduced, which consists of images of tangled linear deformable objects. Method discussed in here was tested on the TANGLED-100 dataset. Accuracy achieved during experiments was found to be 74.9%. Robotic simulations were carried out to demonstrate the use of the proposed method in applications of robot. Proposed method is a general method that can be used by robots working in different situations.; Comment: IEEE International Conference on Image Information Processing; untangle; untangling; computer vision; robotic vision; untangling by robot; Tangled-100 dataset; tangled linear deformable objects; personal robotics; image processing

CloudCV: Large Scale Distributed Computer Vision as a Cloud Service

Agrawal, Harsh; Mathialagan, Clint Solomon; Goyal, Yash; Chavali, Neelima; Banik, Prakriti; Mohapatra, Akrit; Osman, Ahmed; Batra, Dhruv
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/06/2015
Relevância na Pesquisa
66.24%
We are witnessing a proliferation of massive visual data. Unfortunately scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic, logistical, and infrastructural problems. Our goal is to democratize computer vision; one should not have to be a computer vision, big data and distributed computing expert to have access to state-of-the-art distributed computer vision algorithms. We present CloudCV, a comprehensive system to provide access to state-of-the-art distributed computer vision algorithms as a cloud service through a Web Interface and APIs.

Cloud Computing framework for Computer Vision Research:An Introduction

Zhou, Yu
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/02/2013
Relevância na Pesquisa
66.17%
Cloud computing offers the potential to help scientists to process massive number of computing resources often required in machine learning application such as computer vision problems. This proposal would like to show that which benefits can be obtained from cloud in order to help medical image analysis users (including scientists, clinicians, and research institutes). As security and privacy of algorithms are important for most of algorithms inventors, these algorithms can be hidden in a cloud to allow the users to use the algorithms as a package without any access to see/change their inside. In another word, in the user part, users send their images to the cloud and configure the algorithm via an interface. In the cloud part, the algorithms are applied to this image and the results are returned back to the user. My proposal has two parts: (1) investigate the potential of cloud computing for computer vision problems and (2) study the components of a proposed cloud-based framework for medical image analysis application and develop them (depending on the length of the internship). The investigation part will involve a study on several aspects of the problem including security, usability (for medical end users of the service), appropriate programming abstractions for vision problems...

Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision

Xiong, Xuehan; De la Torre, Fernando
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/05/2014
Relevância na Pesquisa
66.17%
Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved with nonlinear optimization methods. It is generally accepted that second order descent methods are the most robust, fast, and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, second order descent methods have two main drawbacks: (1) the function might not be analytically differentiable and numerical approximations are impractical, and (2) the Hessian may be large and not positive definite. To address these issues, this paper proposes generic descent maps, which are average "descent directions" and rescaling factors learned in a supervised fashion. Using generic descent maps, we derive a practical algorithm - Supervised Descent Method (SDM) - for minimizing Nonlinear Least Squares (NLS) problems. During training, SDM learns a sequence of decent maps that minimize the NLS. In testing, SDM minimizes the NLS objective using the learned descent maps without computing the Jacobian or the Hessian. We prove the conditions under which the SDM is guaranteed to converge. We illustrate the effectiveness and accuracy of SDM in three computer vision problems: rigid image alignment...

Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision

Wolff, J. Gerard
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
66.2%
The SP theory of intelligence aims to simplify and integrate concepts in computing and cognition, with information compression as a unifying theme. This article discusses how it may be applied to the understanding of natural vision and the development of computer vision. The theory, which is described quite fully elsewhere, is described here in outline but with enough detail to ensure that the rest of the article makes sense. Low level perceptual features such as edges or corners may be identified by the extraction of redundancy in uniform areas in a manner that is comparable with the run-length encoding technique for information compression. The concept of multiple alignment in the SP theory may be applied to the recognition of objects, and to scene analysis, with a hierarchy of parts and sub-parts, and at multiple levels of abstraction. The theory has potential for the unsupervised learning of visual objects and classes of objects, and suggests how coherent concepts may be derived from fragments. As in natural vision, both recognition and learning in the SP system is robust in the face of errors of omission, commission and substitution. The theory suggests how, via vision, we may piece together a knowledge of the three-dimensional structure of objects and of our environment...

siftservice.com - Turning a Computer Vision algorithm into a World Wide Web Service

Tafti, Ahmad Pahlavan; Hassannia, Hamid; Yu, Zeyun
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 11/04/2015
Relevância na Pesquisa
66.17%
Image features detection and description is a longstanding topic in computer vision and pattern recognition areas. The Scale Invariant Feature Transform (SIFT) is probably the most popular and widely demanded feature descriptor which facilitates a variety of computer vision applications such as image registration, object tracking, image forgery detection, and 3D surface reconstruction. This work introduces a Software as a Service (SaaS) based implementation of the SIFT algorithm which is freely available at http://siftservice.com for any academic, educational and research purposes. The service provides application-to-application interaction and aims Rapid Application Development (RAD) and also fast prototyping for computer vision students and researchers all around the world. An Internet connection is all they need!; Comment: 8 pages, 7 figures

Utility-Based Control for Computer Vision

Levitt, Tod S.; Binford, Thomas O.; Ettinger, Gil J.; Gelband, Patrice
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 27/03/2013
Relevância na Pesquisa
66.22%
Several key issues arise in implementing computer vision recognition of world objects in terms of Bayesian networks. Computational efficiency is a driving force. Perceptual networks are very deep, typically fifteen levels of structure. Images are wide, e.g., an unspecified-number of edges may appear anywhere in an image 512 x 512 pixels or larger. For efficiency, we dynamically instantiate hypotheses of observed objects. The network is not fixed, but is created incrementally at runtime. Generation of hypotheses of world objects and indexing of models for recognition are important, but they are not considered here [4,11]. This work is aimed at near-term implementation with parallel computation in a radar surveillance system, ADRIES [5, 15], and a system for industrial part recognition, SUCCESSOR [2]. For many applications, vision must be faster to be practical and so efficiently controlling the machine vision process is critical. Perceptual operators may scan megapixels and may require minutes of computation time. It is necessary to avoid unnecessary sensor actions and computation. Parallel computation is available at several levels of processor capability. The potential for parallel, distributed computation for high-level vision means distributing non-homogeneous computations. This paper addresses the problem of task control in machine vision systems based on Bayesian probability models. We separate control and inference to extend the previous work [3] to maximize utility instead of probability. Maximizing utility allows adopting perceptual strategies for efficient information gathering with sensors and analysis of sensor data. Results of controlling machine vision via utility to recognize military situations are presented in this paper. Future work extends this to industrial part recognition for SUCCESSOR.; Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (UAI1988)

Addressing the non-functional requirements of computer vision systems: A case study

Fenn, Shannon; Mendes, Alexandre; Budden, David
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 30/10/2014
Relevância na Pesquisa
66.25%
Computer vision plays a major role in the robotics industry, where vision data is frequently used for navigation and high-level decision making. Although there is significant research in algorithms and functional requirements, there is a comparative lack of emphasis on how best to map these abstract concepts onto an appropriate software architecture. In this study, we distinguish between the functional and non-functional requirements of a computer vision system. Using a RoboCup humanoid robot system as a case study, we propose and develop a software architecture that fulfills the latter criteria. The modifiability of the proposed architecture is demonstrated by detailing a number of feature detection algorithms and emphasizing which aspects of the underlying framework were modified to support their integration. To demonstrate portability, we port our vision system (designed for an application-specific DARwIn-OP humanoid robot) to a general-purpose, Raspberry Pi computer. We evaluate performance on both platforms and compare them to a vision system optimised for functional requirements only. The architecture and implementation presented in this study provide a highly generalisable framework for computer vision system design that is of particular benefit in research and development...

Low Cost, High Precision, Autonomous Measurement of Trunk Diameter based on Computer Vision

Pérez, Diego Sebastián; Bromberg, Facundo; Antivilo, Francisco Gonzalez
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/05/2014
Relevância na Pesquisa
66.2%
Trunk diameter is a variable of agricultural interest, used mainly in the prediction of fruit trees production. It is correlated with leaf area and biomass of trees, and consequently gives a good estimate of potential production of the plants. This work presents a low cost, high precision method for autonomous measurement of trunk diameter of fruit trees based on Computer Vision. Autonomous methods based on Computer Vision or other techniques are introduced in the literature for they present important simplifications in the measurement process, requiring little to none human decision making. This presents different advantages for crop management: the method is amenable to be operated by unknowledgeable personnel, with lower operational costs; it results in lower stress levels to knowledgeable personnel, avoiding the deterioration of the measurement quality over time; or it makes the measurement process amenable to be embedded in larger autonomous systems, allowing more measurement to be taken with equivalent costs. In a more personal aspect, the present work is also a successful proof-of-concept for our laboratories and regional research institutions in favor of autonomous measurements based on Computer Vision, opening the door to further investigations in other important agronomic variables measurable by Computer Vision. To date...

Communication framework for distributed computer vision on stationary and mobile platforms

Armenio, Christopher
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
66.24%
Recent advances in the complexity and manufacturability of digital video cameras coupled with the ubiquity of high speed computers and communication networks have led to burgeoning research in the fields of computer vision and image understanding. As the generated vision algorithms become increasingly complex, a need arises for robust communication between remote cameras on mobile units and their associated distributed vision algorithms. A communication framework would provide a basis for modularization and abstraction of a collection of computer vision algorithms; the resulting system would allow for straightforward image capture, simplified communication between algorithms, and easy replacement or upgrade of existing component algorithms. The objective of this thesis is to create such a communication framework and demonstrate its viability and applicability by implementing a relatively complex system of distributed computer vision algorithms. These multi-camera algorithms include body tracking, pose estimation and face recognition. Although a plethora of research exists documenting individual algorithms which may utilize multiple networked cameras, this thesis aims to develop a novel way of sharing information between cameras and algorithms in a distributed computation system. In addition...

Sparse signal processing for machine learning and computer vision

Zhou, Yin
Fonte: University of Delaware Publicador: University of Delaware
Tipo: Tese de Doutorado
Relevância na Pesquisa
66.18%
Barner, Kenneth E.; Signal sparse representation solves inverse problems to find succinct expressions of data samples as a linear combination of a few atoms in the dictionary or codebook. This model has proven effective in image restoration, denoising, inpainting, compression, pattern classification and automatic unsupervised feature learning. Many classical sparse coding algorithms have exorbitant computational complexity in solving the sparse solution, which hinders their applicability in real-world large-scale machine learning and computer vision problems. In this dissertation, we will first present a family of locality-constrained dictionary learning algorithms, which can be seen as a special case of sparse coding. Compared to classical sparse coding, locality-constrained coding has closed-form solution and is much more computationally efficient. In addition, the locality-preserving property enables the newly proposed algorithms to better exploit the geometric structures of data manifold. Experimental results demonstrate that our algorithms are capable of achieving superior classification performance with substantially higher efficiency, compared to sparse-coding based dictionary algorithms. Sparse coding is an effective building block of learning visual features. A good feature representation is critical for machine learning algorithms to achieve satisfactory results. In recent years...