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In given paper offered methods and algorithms of determination of complexity of test questions for formation a database system of the adaptive test control for objective estimation of knowledge of students (pupils) in the course of training learning systems.
This paper focuses on a new type of taxonomy called supervised taxonomy (ST). Supervised taxonomies are generated considering background information concerning class labels in addition to distance metrics, and are capable of capturing class-uniform regions in a dataset. A hierarchical, agglomerative clustering algorithm, called STAXAC that generates STs is proposed and its properties are analyzed...
Issue of using case-based reasoning concerns size control, which can be occurred after reusing the system to fix new set of problems for the next cycle. Accordingly, the algorithm of addition, deletion, partition, and selection are proposed for this reason. However, the problem-based selection model is proposed and offers two main algorithms, which are classifying and deleting obsolete, complex, and...
In this paper, we propose a low-complexity graphic constellation projection (GCP) algorithm for automatic modulation classification (AMC), where the recovered symbols are projected into artificial graphic constellations. Unlike the existing feature- based (FB) algorithms, we convert the AMC problem into an image recognition problem. Subsequently, the deep belief network (DBN) is adopted to learn the...
In this paper we propose and investigate some simplifications to the original Histogram of Oriented Gradients (HOG) algorithm, in order to allow a more efficient hardware implementation, while keeping the overall classification accuracy. The most aggressive simplification is the removal of the bin interpolation step in the algorithm, which does not affect the classification performance, but significantly...
This paper proposes a fast algorithm based on depth map segmentation for the depth coding of 3D video coding to reduce the coding complexity of the depth map. The proposed algorithm segments the depth map into different regions by modifying automatic thresholding technique. We also propose the search range adjustment according to the classification of the coding tree unit (CTU). The early termination...
This paper proposes a new adaptive watermarking algorithm leveraging combined knowledge of spatial and frequency domains to ensure the robustness and security of the embedded watermark and the transparency of the carrier image. The scheme makes use of the perceptual characteristics of human visual system and the local correlation of the image. The wavelet coefficients of the image are divided into...
Odor source localization with mobile robots has recently been subject to many research works, but remains a challenging task mainly due to the large number of environmental parameters that make it hard to describe gas concentration fields. We designed a new algorithm called Adaptive Lévy Taxis (ALT) to achieve odor plume tracking through a correlated random walk. In order to compare its performances...
Breast cancer is invasive cancer among world's women above 35 years of age. The most common symptoms of breast cancer are lumps, change in shape/skin colour and liquid oozing out from nipple. Breast cancer mostly starts from breast tissues that are either in lobules or in milk ducts. Ductal carcinoma is the common type of breast cancer starts from milk ducts and spread across the. Women between the...
Algorithm analysis is an important part of algorithm design. Traditionally, analysis of programming code or algorithms is theoretical and mathematical. This makes it a time consuming and manual process. This limits the scope and scale of undertaking such a task. There has always been an ever-growing need to automate this analysis. With mobile development taking the center stage, we need to ensure...
Online social message classification is an important task for E-Commerce companies to mine and classify the customer opinions. In this paper, we have proposed a first of its kind of an efficient message classification algorithm which is independent of tweet content and considers the set of followers who will retweet during the retweet peaks. By including the followers who will retweet during retweet...
The paper is devoted to data mining method developing for data analysis in a supermarket's database to determine how product should be located on shelves.
High Efficiency Video Coding (HEVC) achieves the most significant coding efficiency compared with all the existing video coding standards. However, the Intra encoding complexity is increased dramatically since the complex recursive search algorithm for the coding unit (CU) size decisions. In this paper, a fast CU size decision algorithm based on Support Vector Machines (SVM) is proposed to further...
The condensed nearest neighbor algorithm(CNN) is susceptible to pattern read sequence, abnormal patterns and so on. To deal with the above problems, through the analysis of the relationship between the whole dataset and the individual patterns, a new prototype selection algorithm is proposed based on the extended near neighbor relationship and the affinity changes. First, the proposed algorithm can...
We present a novel method for classifying hashtag types. Specifically, we apply word segmentation algorithms and lexical resources in order to classify two types of hashtags: those with sentiment information and those without. However, the complex structure of hashtags increases the difficulty of identifying sentiment information. In order to solve this problem, we segment hashtags into smaller semantic...
In the medical decision field, the conventional analysis methods of medical insurance data are lack of flexibility and efficiency. When coping with huge and redundant medical dataset, it is difficult to extract the candidate attributes if only using some limited professional knowledge. Therefore, the correlations between the medical insurance cost and relevant factors (e.g. diagnosis results) need...
In Machine Learning, we often encounter instances of imbalanced data which occur whenever there is an unequal representation in the classification categories. New found interest in Machine Learning has made its usage ubiquitous. Its applications encompass a wide plethora of scenarios ranging from Business and Banking to Bioinformatics and Psychology. These problems are often characterized by imbalanced...
Classifier allows the user to classify between different classes based on the features acquired. The goals and applications of different classifiers are different. As the feature selection is one of the important criteria. In this paper we introduce a method of ranking the features of one class with respect to another and it tells the user that in the training set which feature has higher ranking...
When capturing images in real environments, there's an incidence of external and internal adverse factors. These factors can cause negative changes in the captured graphical results. By implementing enhancement techniques used in digital image processing, it is possible to achieve an output of greater detail and an attenuation of affectation components. An algorithmic proposal for median spatial filtering...
Host Based Intrusion Detection Systems (HIDS) are gaining traction in discovering malicious software inside a host operating system. In this paper, the authors have developed a new cognitive host based anomaly detection system based on supervised AdaBoost machine learning algorithm. Particularly, information fractal dimension based approach is incorporated in the original AdaBoost machine learning...
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