9 research outputs found
Aesthetic Preference Prediction in Interior Design: Fuzzy Approach
Interior design is all about creating spaces that look and feel good.
However, the subjective nature of aesthetic preferences presents a significant
challenge in defining and quantifying what makes an interior design visually
appealing. The current paper addresses this gap by introducing a novel
methodology for quantifying and predicting aesthetic preferences in interior
design. Our study combines fuzzy logic with image processing techniques. We
collected a dataset of interior design images from social media platforms,
focusing on essential visual attributes such as color harmony, lightness, and
complexity. We integrate these features using weighted average to compute a
general aesthetic score. Our approach considers individual color preferences in
calculating the overall aesthetic preference. We initially gather user ratings
for primary colors like red, brown, and others to understand their preferences.
Then, we use the pixel count of the top five dominant colors in the image to
get the color scheme preference. The color scheme preference and the aesthetic
score are then passed as inputs to the fuzzy inference system to calculate an
overall preference score. This score represents a comprehensive measure of the
user's preference for a particular interior design, considering their color
choices and general aesthetic appeal. We used the 2AFC (Two-Alternative Forced
Choice) method to validate our methodology, achieving a notable hit rate of
0.7. This study can help designers and professionals better understand and meet
people's interior design preferences, especially in a world that relies heavily
on digital media.Comment: Submitted to IEEE conference for consideratio
Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children
Computer games are widespread nowadays and enjoyed by people of all ages. But
when it comes to kids, playing these games can be more than just fun, it is a
way for them to develop important skills and build emotional intelligence.
Facial expressions and sounds that kids produce during gameplay reflect their
feelings, thoughts, and moods. In this paper, we propose a novel framework that
integrates a fuzzy approach for the recognition of emotions through the
analysis of audio and video data. Our focus lies within the specific context of
computer games tailored for children, aiming to enhance their overall user
experience. We use the FER dataset to detect facial emotions in video frames
recorded from the screen during the game. For the audio emotion recognition of
sounds a kid produces during the game, we use CREMA-D, TESS, RAVDESS, and Savee
datasets. Next, a fuzzy inference system is used for the fusion of results.
Besides this, our system can detect emotion stability and emotion diversity
during gameplay, which, together with prevailing emotion report, can serve as
valuable information for parents worrying about the effect of certain games on
their kids. The proposed approach has shown promising results in the
preliminary experiments we conducted, involving 3 different video games, namely
fighting, racing, and logic games, and providing emotion-tracking results for
kids in each game. Our study can contribute to the advancement of
child-oriented game development, which is not only engaging but also accounts
for children's cognitive and emotional states.Comment: 8 pages. Prepared for the Elsevier conferenc
Detection and Analysis of Stress-Related Posts in Reddit Acamedic Communities
Nowadays, the significance of monitoring stress levels and recognizing early
signs of mental illness cannot be overstated. Automatic stress detection in
text can proactively help manage stress and protect mental well-being. In
today's digital era, social media platforms reflect the psychological
well-being and stress levels within various communities. This study focuses on
detecting and analyzing stress-related posts in Reddit academic communities.
Due to online education and remote work, these communities have become central
for academic discussions and support. We classify text as stressed or not using
natural language processing and machine learning classifiers, with Dreaddit as
our training dataset, which contains labeled data from Reddit. Next, we collect
and analyze posts from various academic subreddits. We identified that the most
effective individual feature for stress detection is the Bag of Words, paired
with the Logistic Regression classifier, achieving a 77.78% accuracy rate and
an F1 score of 0.79 on the DReaddit dataset. This combination also performs
best in stress detection on human-annotated datasets, with a 72% accuracy rate.
Our key findings reveal that posts and comments in professors Reddit
communities are the most stressful, compared to other academic levels,
including bachelor, graduate, and Ph.D. students. This research contributes to
our understanding of the stress levels within academic communities. It can help
academic institutions and online communities develop measures and interventions
to address this issue effectively.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Fuzzy Color Space for Apparel Coordination
Human perception of colors constitutes an important part in color theory. The applications of color science are truly omnipresent, and what impression colors make on human plays a vital role in them. In this paper, we offer the novel approach for color information representation and processing using fuzzy sets and logic theory, which is extremely useful in modeling human impressions. Specifically, we use fuzzy mathematics to partition the gamut of feasible colors in HSI color space based on standard linguistic tags. The proposed method can be useful in various image processing applications involving query processing. We demonstrate its effectivity in the implementation of a framework for the apparel online shopping coordination based on a color scheme. It deserves attention, since there is always some uncertainty inherent in the description of apparels
Towards a Universal Understanding of Color Harmony: Fuzzy Approach
Harmony level prediction is receiving increasing attention nowadays. Color
plays a crucial role in affecting human aesthetic responses. In this paper, we
explore color harmony using a fuzzy-based color model and address the question
of its universality. For our experiments, we utilize a dataset containing
attractive images from five different domains: fashion, art, nature, interior
design, and brand logos. We aim to identify harmony patterns and dominant color
palettes within these images using a fuzzy approach. It is well-suited for this
task because it can handle the inherent subjectivity and contextual variability
associated with aesthetics and color harmony evaluation. Our experimental
results suggest that color harmony is largely universal. Additionally, our
findings reveal that color harmony is not solely influenced by hue
relationships on the color wheel but also by the saturation and intensity of
colors. In palettes with high harmony levels, we observed a prevalent adherence
to color wheel principles while maintaining moderate levels of saturation and
intensity. These findings contribute to ongoing research on color harmony and
its underlying principles, offering valuable insights for designers, artists,
and researchers in the field of aesthetics.Comment: Submitted to FSDM 2023 - The 9th International Conference on Fuzzy
Systems and Data Minin
Intelligent System for Assessing University Student Personality Development and Career Readiness
While academic metrics such as transcripts and GPA are commonly used to
evaluate students' knowledge acquisition, there is a lack of comprehensive
metrics to measure their preparedness for the challenges of post-graduation
life. This research paper explores the impact of various factors on university
students' readiness for change and transition, with a focus on their
preparedness for careers. The methodology employed in this study involves
designing a survey based on Paul J. Mayer's "The Balance Wheel" to capture
students' sentiments on various life aspects, including satisfaction with the
educational process and expectations of salary. The collected data from a KBTU
student survey (n=47) were processed through machine learning models: Linear
Regression, Support Vector Regression (SVR), Random Forest Regression.
Subsequently, an intelligent system was built using these models and fuzzy
sets. The system is capable of evaluating graduates' readiness for their future
careers and demonstrates a high predictive power. The findings of this research
have practical implications for educational institutions. Such an intelligent
system can serve as a valuable tool for universities to assess and enhance
students' preparedness for post-graduation challenges. By recognizing the
factors contributing to students' readiness for change, universities can refine
curricula and processes to better prepare students for their career journeys.Comment: 8 pages. Submitted to Elsevier conferenc
Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data
Social media is now the predominant source of information due to the availability of immediate public response. As a result, social media data has become a valuable resource for comprehending public sentiments. Studies have shown that it can amplify ideas and influence public sentiments. This study analyzes the public perception of climate change and the environment over a decade from 2014 to 2023. Using the Pointwise Mutual Information (PMI) algorithm, we identify sentiment and explore prevailing emotions expressed within environmental tweets across various social media platforms, namely Twitter, Reddit, and YouTube. Accuracy on a human-annotated dataset was 0.65, higher than Vader’s score but lower than that of an expert rater (0.90). Our findings suggest that negative environmental tweets are far more common than positive or neutral ones. Climate change, air quality, emissions, plastic, and recycling are the most discussed topics on all social media platforms, highlighting its huge global concern. The most common emotions in environmental tweets are fear, trust, and anticipation, demonstrating the wide and complex nature of public reactions. By identifying patterns and trends in opinions related to the environment, we hope to provide insights that can help raise awareness regarding environmental issues, inform the development of interventions, and adapt further actions to meet environmental challenges