Fusion Fuzzy Logic and Deep Learning for Depression Detection Using Facial Expressions

Rajawat, Anand Singh; Bedi, Pradeep; Goyal, Shreyas; Bhaladhare, Pawan; Aggarwal, Alok; Singhal, Reena

doi:10.1016/j.procs.2023.01.251

Cited by 13 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While various models and techniques have been developed for anxiety prediction, this study introduces a fuzzy logic-based classification model, specifically designed to capture the subjectivity and uncertainty inherent in anxiety assessment. The proposed system aims to deliver accurate predictions in the early stages of anxiety disorder [37] .…”

Section: Related Workmentioning

confidence: 99%

Some distance measures for pythagorean cubic fuzzy sets: Application selection in optimal treatment for depression and anxiety

Rahim,

Amin,

Shah

et al. 2024

MethodsX

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Some distance measures for pythagorean cubic fuzzy sets: Application selection in optimal treatment for depression and anxiety

Rahim,

Amin,

Shah

et al. 2024

MethodsX

View full text Add to dashboard Cite

“…DASS 42 is a questionnaire with 42 questions, consisting of 3 emotional scales: depression, anxiety, and stress with levels as shown in Table 5; normal, mild, moderate, severe, and very severe [31]. The DASS 42 scale can be classified into 3 [32], which are the depression scale (questions number 3,5,10,13,16,17,21,24,26,31,34,37,38,42), the anxiety scale (questions number 2, 4, 7, 9, 15, 19, 20, 23, 25, 28, 30, 36, 40, 41.3), and the stress scale (questions number 1,6,8,11,12,14,18,22,27,29,32,33,35,39).…”

Section: Dass 42 Testmentioning

confidence: 99%

“…The fuzzy logic method is a method that can process variables that are fuzzy or cannot be described with certainty [16]. The advantages of the fuzzy logic method are that it is easy to understand because it uses the basis of set theory, is very flexible, meaning that it is able to adapt to changes and uncertainties that accompany problems, has tolerance for inaccurate data, is able to model very complex nonlinear functions [17], can build and apply the experiences of experts directly without having to go through a training process which is often known as the fuzzy expert system, can work with conventional control techniques [18]- [20]. Fuzzy logic is also based on everyday language, so it is easy to understand [21].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic method-based stress detector with blood pressure and body temperature parameters

Fajrin,

Sasmeri,

Riski Prilia

et al. 2024

IJECE

View full text Add to dashboard Cite

In this study, using the fuzzy logic method, a stress detection tool was created with body temperature and blood pressure parameters as indicators to determine a person's stress level. This tool uses the LM35DZ sensor to detect body temperature, the MPX5100GP sensor to read blood pressure values, and Arduino Uno as a data processor from sensor readings which are then calculated using the fuzzy logic method as a stress level decision-maker. The resulting output measures blood pressure, body temperature, and the stress level experienced by a person, which will be displayed on the liquid crystal display. Based on the results of testing the body temperature parameter, the highest error generated was 1.17%, and for the blood pressure parameter, the highest error was 2.5% for systole and 0.93% for diastole. Furthermore, testing the stress level displayed on the tool is compared to the depression, anxiety, and stress scales 42 (DASS 42), a psychological stress measuring instrument. From the results of testing the tool with the questionnaire, the average conformity level is 74%.

show abstract

“…In another research, advanced fuzzy algorithm has been used for detection of depression in people. The model uses fuzzy logic and convolution neural network to use facial expressions for detection of depressed state [64].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

Jatain,

Niranjanamurthy,

Dayananda

2024

SN COMPUT. SCI.

View full text Add to dashboard Cite

With more and more teaching learning activities being shifted to online mode, the education system has seen a drastic paradigm shift in the recent times. Learner opinion has emerged as an important metric for gaining valuable insights about teaching–learning process, student satisfaction, course popularity, etc. Traditional methods for opinion mining of learner feedback are tedious and require manual intervention. The author, in this work has proposed a hybrid bio-inspired metaheuristic feature selection approach for opinion mining of learner comments regarding a course. Experimental work is conducted over a real-world education dataset comprising of 110 K learner comments (referred to as Educational Dataset now onwards) collected from Coursera and learner data from academic institution MSIT. Based on the experimental results over the collected dataset, the proposed model achieves an accuracy of 92.24%. Further, for comparative analysis, results of the proposed model are compared with the ENN models for different embeddings, viz., Word2Vec, tf-idf and domain-specific embedding for the SemEval-14 Task 4. The hybrid bio-inspired metaheuristic model outperforms the pre-existing models for the standard dataset too.

show abstract

Fusion Fuzzy Logic and Deep Learning for Depression Detection Using Facial Expressions

Cited by 13 publications

References 10 publications

Some distance measures for pythagorean cubic fuzzy sets: Application selection in optimal treatment for depression and anxiety

Some distance measures for pythagorean cubic fuzzy sets: Application selection in optimal treatment for depression and anxiety

Fuzzy logic method-based stress detector with blood pressure and body temperature parameters

A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

Contact Info

Product

Resources

About