A Fuzzy C-means News Article Clustering Based on an Improved Sqrt-Cosine Similarity Measurement

2022 ◽  
Vol 22 (1) ◽  
pp. 21
Author(s):  
Kayode Olaseni ◽  
Salisu Aliyu ◽  
Kareem Bakare
Keyword(s):  
Cosine Similarity ◽  
News Article ◽  
Similarity Measurement ◽  
Fuzzy C Means

scholarly journals Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 696
Author(s):  
Haipeng Chen ◽  
Zeyu Xie ◽  
Yongping Huang ◽  
Di Gai
Keyword(s):  
Brain Tumor ◽  
Information Transfer ◽  
Magnetic Resonance Images ◽  
Low Noise ◽  
Similarity Measurement ◽  
Noise Sensitivity ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Membership Information ◽  
Fuzzy C Means Algorithm

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.

scholarly journals Recommender System using Content Based Filtering for News Portal in Indonesia

2020 ◽  
Vol 8 (6) ◽  
pp. 173-178
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Recommender System ◽  
Information Overload ◽  
Cosine Similarity ◽  
News Article ◽  
The Internet ◽  
New Model ◽  
Digital Era ◽  
Content Based Filtering

Nowadays there is much news on the internet. It makes the reader become information overload. The reader does not know the most important news for them. The digital era, especially in Indonesia, generated data in Bahasa very fast that referred to as big data. Data mining by process big data can collect the data insight that the reader already read. This paper proposes a new model to proceed with Bahasa news and use the TF-IDF method to collect the feature of the article. Cosine similarity from the news article used to rank the new unknown articles to recommend articles based on their preference. we can filtering the stream of information and highlight the most likely article they will read but based on their preference that we already collect implicitly from the article that they read it, it’s a scroll depth of the article they read.Then we can serve the news more personalized from what they love to read.

scholarly journals Improved sqrt-cosine similarity measurement

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Sahar Sohangir ◽  
Dingding Wang
Keyword(s):  
Cosine Similarity ◽  
Similarity Measurement

Examining the Effects of Two Mindful Eating Exercises on Chocolate Consumption

2019 ◽  
Vol 26 (4) ◽  
pp. 120-128 ◽  
Author(s):  
Michael Mantzios ◽  
Kirby Skillett ◽  
Helen Egan
Keyword(s):  
Likert Scale ◽  
News Article ◽  
Mindful Eating ◽  
The Impact

Abstract. The present study aimed to investigate and compare the impact of the Mindful Construal Diary (MCD) and the Mindful Raisin Exercise on the sensory tasting experience of chocolate and participants’ chocolate consumption. Participants were randomly allocated into three conditions (MCD, mindful raisin exercise, and mindless control), and engaged with either the MCD, the mindful raisin exercise, or, were asked to read a news article, respectively, while they ate a piece of chocolate. They then rated their satisfaction and desire to consume more chocolate on a 10-point Likert scale, and filled in a state mindful eating scale. Afterward, participants were informed that the study had ended and were asked to wait while the experimenter recorded some information, and any extra chocolate consumption during this time was recorded. Participants in both mindfulness conditions consumed significantly less chocolate after the exercise than participants in the control condition. No significant differences were found between the three conditions on ratings of satisfaction and desire to consume more chocolate. Both the MCD and the raisin exercise can be used to successfully moderate the intake of calorific foods, while the MCD can be utilized as an alternative practice to the typical meditation-based interventions.

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