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Data analytics have become increasingly complicated as the amount of data has increased. One technique that is used to enable data analytics in large datasets is data sampling, in which a portion of the data is selected to preserve the data characteristics for use in data analytics. In this paper, we introduce a novel data sampling technique that is rooted in formal concept analysis theory. This technique...
Most modern search engines feature keyword based search interfaces. These interfaces are usually found on websites belonging to enterprises or governments or sites related to news articles, blogs and social media that contain a large corpus of documents. These collections of documents are not easily indexed by web search engines, and are considered as hidden web databases. These databases provide...
This paper focuses on detecting inconsistencies within text corpora. It is a very interesting area with many applications. Most existing methods deal with this problem using complicated textual analysis, which is known for not being accurate enough. We propose a new methodology that consists of two steps, the first one being a machine learning step that performs multilevel text categorization. The...
Online marketplaces are e-commerce websites where thousands of products are provided by multiple third parties. There are dozens of these differently structured marketplaces that need to be visited by the end users to reach their targets. This searching process consumes a lot of time and effort; moreover it negatively affects the user experience. In this paper, extensive analysis and evaluation of...
The task of whole-body motion planning for humanoid robots is challenging due to its high-DOF nature, stability constraints, and the need for obstacle avoidance and movements that are efficient. Over the years, various approaches have been adopted to solve this problem such as bounding-box models and jacobian-based techniques. More commonly though, sampling-based algorithms are employed for this task...
Knowledge discovery from data is a challenging problem that has significant importance in many different fields such as biology, economics and social sciences. Real-world data is incomplete and ambiguous; moreover, its rapid increase in size complicates the analysis process. Therefore, data reduction techniques that consider data uncertainty are highly required. In this paper, our objective is to...
The benefits of bidirectional planning over the unidirectional version are well established for motion planning in high-dimensional configuration spaces. While bidirectional approaches have been employed with great success in the context of sampling-based planners such as in RRT-Connect, they have not enjoyed popularity amongst search-based methods such as A*. The systematic nature of search-based...
Aim of this paper is to propose a methodology of real-time hand detection based on skin color model and background subtraction under any complex background with extracting the depth information. With the use of stereo camera calibration and disparity mapping, the depth information of the hand is extracted. Reasonable selection of threshold of skin color model and combining with background difference...
A methodology is presented to develop motions for the purposes of a humanoid goal keeper robot during a match of soccer. These motions meet performance objectives as well as minimize damage to the humanoid robot that occurs during the execution of the motion. The methodology presented employs the use of a realistic simulator paired with controlled human influence. The resulting motions better meet...
Rapidly exploring Random Trees (RRT), a sampling based algorithm, efficiently computes a path between a start and a goal configuration. RRT-Connect, is a variant of RRT that works by incrementally building two RRTs rooted at the start and the goal configurations. Significant amount of research has been done on the motion planning of six-legged robots. We improve upon a certain technique which employs...
Many motion planning problems in robotics are high dimensional planning problems. While sampling-based motion planning algorithms handle the high dimensionality very well, the solution qualities are often hard to control due to the inherent randomization. In addition, they suffer severely when the configuration space has several ‘narrow passages’. Search-based planners on the other hand typically...
Recently proposed Rapidly Exploring Random Tree Star (RRT*) algorithm which is an extension of Rapidly Exploring Random Tree (RRT) provides collision free asymptotically optimal path regardless of obstacle's geometry in a given environment. However, the drawback of this technique is a slow processing rate. This paper presents our proposed Potential Guided Directional-RRT* which addresses this problem...
Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. However, it cannot guarantee finding the most optimal path. A recently proposed extension of RRT, known as Rapidly Exploring Random Tree Star (RRT∗), claims to achieve convergence towards the optimal solution but has been proven to take an infinite time to do so and with a slow convergence...
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