scholarly journals Cosine similarity-based algorithm for social networking recommendation

2022 ◽  
Vol 12 (2) ◽  
pp. 1881
Author(s):  
Shaha Al-Otaibi ◽  
Nourah Altwoijry ◽  
Alanoud Alqahtani ◽  
Latifah Aldheem ◽  
Mohrah Alqhatani ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Social Networking ◽  
Experimental Studies ◽  
Real Life ◽  
Cosine Similarity ◽  
User Profiling ◽  
Special Groups ◽  
Extraction Algorithm ◽  
Cosine Similarity Measure ◽  
Negative Messages

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities.

Detecting Crew Alertness With Processed Speech

2007 ◽  
Author(s):  
Samuel K. Shimp ◽  
Steve C. Southward ◽  
Mehdi Ahmadian
Keyword(s):  
Feature Extraction ◽  
Monitoring System ◽  
Vocal Tract ◽  
Real Life ◽  
Transportation Systems ◽  
The Core ◽  
Communication Signal ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Speech Features

This paper proposes a solution for improving the safety of rail and other mass transportation systems through operator alertness monitoring. A non-invasive method of alertness monitoring through speech processing is presented. Speech analysis identifies measurable vocal tract changes due to fatigue and decreased speech rate due to decreased mental ability. Enabled by existing noise reduction technology, a system has been designed for measuring key speech features that are believed to correlate to alertness level. The features of interest are pitch, word intensity, pauses between words and phrases, and word rate. The purpose of this paper is to describe the overall alertness monitoring system design and then to show some experimental results for the core processing algorithm which extracts features from the speech. The feature extraction algorithm proposed here uses a new and simple technique to parse the continuous speech signal coming from the communication signal without using computationally demanding and error-prone word recognition techniques. Preliminary results on the core feature extraction algorithm indicate that words, phrases, and rates can be determined for relatively noise-free speech signals. Once the remainder of the overall alertness monitoring system is complete, it will be applied to real life recordings of train operators and will be subjected to clinical testing to determine alert and non-alert levels of the speech features of interest.

Corresponding Feature Extraction Algorithm between Infrared and Visible Images Using MSER

2011 ◽  
Vol 33 (7) ◽  
pp. 1625-1631 ◽  
Author(s):  
Lin Lian ◽  
Guo-hui Li ◽  
Hai-tao Wang ◽  
hao Tian ◽  
Shu-kui Xu
Keyword(s):  
Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Visible Images

A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification

2012 ◽  
Vol 19 (10) ◽  
pp. 639-642 ◽  
Author(s):  
Qianwei Zhou ◽  
Guanjun Tong ◽  
Dongfeng Xie ◽  
Baoqing Li ◽  
Xiaobing Yuan
Keyword(s):  
Feature Extraction ◽  
Target Classification ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Ground Target

scholarly journals The Influence of Alcohol Consumption on Fighting, Shoplifting and Vandalism in Young Adults

2021 ◽  
Vol 18 (7) ◽  
pp. 3509
Author(s):  
Ieuan Evans ◽  
Jon Heron ◽  
Joseph Murray ◽  
Matthew Hickman ◽  
Gemma Hammerton
Keyword(s):  
Young Adults ◽  
Longitudinal Study ◽  
Alcohol Consumption ◽  
Experimental Studies ◽  
Real Life ◽  
Self Report ◽  
Parents And Children ◽  
Experimental Findings ◽  
Avon Longitudinal Study ◽  
Causal Component

Experimental studies support the conventional belief that people behave more aggressively whilst under the influence of alcohol. To examine how these experimental findings manifest in real life situations, this study uses a method for estimating evidence for causality with observational data—‘situational decomposition’ to examine the association between alcohol consumption and crime in young adults from the Avon Longitudinal Study of Parents and Children. Self-report questionnaires were completed at age 24 years to assess typical alcohol consumption and frequency, participation in fighting, shoplifting and vandalism in the previous year, and whether these crimes were committed under the influence of alcohol. Situational decomposition compares the strength of two associations, (1) the total association between alcohol consumption and crime (sober or intoxicated) versus (2) the association between alcohol consumption and crime committed while sober. There was an association between typical alcohol consumption and total crime for fighting [OR (95% CI): 1.47 (1.29, 1.67)], shoplifting [OR (95% CI): 1.25 (1.12, 1.40)], and vandalism [OR (95% CI): 1.33 (1.12, 1.57)]. The associations for both fighting and shoplifting had a small causal component (with the association for sober crime slightly smaller than the association for total crime). However, the association for vandalism had a larger causal component.

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