77 research outputs found
Fuzzification-based Feature Selection for Enhanced Website Content Encryption
We propose a novel approach that utilizes fuzzification theory to perform
feature selection on website content for encryption purposes. Our objective is
to identify and select the most relevant features from the website by
harnessing the principles of fuzzy logic. Fuzzification allows us to transform
the crisp website content into fuzzy representations, enabling a more nuanced
analysis of their characteristics. By considering the degree of membership of
each feature in different fuzzy categories, we can evaluate their importance
and relevance for encryption. This approach enables us to prioritize and focus
on the features that exhibit higher membership degrees, indicating their
significance in the encryption process. By employing fuzzification-based
feature selection, we aim to enhance the effectiveness and efficiency of
website content encryption, ultimately improving the overall internet security
An Accurate Facial Component Detection Using Gabor Filter
Face detection is a critical task to be resolved in a variety of applications. Since faces include various expressions it becomes a difficult task to detect the exact output. Face detection not only play a main role in personal identification but also in various fields which includes but not limited to image processing, pattern recognition, graphics and other application areas. The proposed system performs the face detection and facial components using Gabor filter. The results show accurate detection of facial component
Modern drowsiness detection techniques: a review
According to recent statistics, drowsiness, rather than alcohol, is now responsible for one-quarter of all automobile accidents. As a result, many monitoring systems have been created to reduce and prevent such accidents. However, despite the huge amount of state-of-the-art drowsiness detection systems, it is not clear which one is the most appropriate. The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion of the advantages and disadvantages of each technique that has been evaluated, and lastly the recommendation of a new strategy for detecting drowsiness
A Modified Version of the K-means Clustering Algorithm
Clustering is a technique in data mining which divides given data set into small clusters based on their similarity. K-means clustering algorithm is a popular, unsupervised and iterative clustering algorithm which divides given dataset into k clusters. But there are some drawbacks of traditional k-means clustering algorithm such as it takes more time to run as it has to calculate distance between each data object and all centroids in each iteration. Accuracy of final clustering result is mainly depends on correctness of the initial centroids, which are selected randomly. This paper proposes a methodology which finds better initial centroids further this method is combined with existing improved method for assigning data objects to clusters which requires two simple data structures to store information about each iteration, which is to be used in the next iteration. Proposed algorithm is compared in terms of time and accuracy with traditional k-means clustering algorithm as well as with a popular improved k-means clustering algorithm
Data Stream Clustering: Challenges and Issues
Very large databases are required to store massive amounts of data that are
continuously inserted and queried. Analyzing huge data sets and extracting
valuable pattern in many applications are interesting for researchers. We can
identify two main groups of techniques for huge data bases mining. One group
refers to streaming data and applies mining techniques whereas second group
attempts to solve this problem directly with efficient algorithms. Recently
many researchers have focused on data stream as an efficient strategy against
huge data base mining instead of mining on entire data base. The main problem
in data stream mining means evolving data is more difficult to detect in this
techniques therefore unsupervised methods should be applied. However,
clustering techniques can lead us to discover hidden information. In this
survey, we try to clarify: first, the different problem definitions related to
data stream clustering in general; second, the specific difficulties
encountered in this field of research; third, the varying assumptions,
heuristics, and intuitions forming the basis of different approaches; and how
several prominent solutions tackle different problems. Index Terms- Data
Stream, Clustering, K-Means, Concept driftComment: IMECS201
- …