Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage

Mishra, Pragnyaban; Srinivas, P V V S

doi:10.11591/ijai.v10.i4.pp889-900

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…They show the influence of imagery in amplifying the effects that resonate with the challenge of discerning truth in the digital age. Srinivas and Mishra (2022), Mishra and Srinivas (2021), Setiawan et al (2022) examine SA techniques in predicting electoral outcomes based on SM moods.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of social media on the evolution of English semantics through linguistic analysis

Shen

2024

Forum Linguist. Stud.

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Social media (SM) influences social interaction in the age of digital media, impacting how languages develop. Since these networks play a role in daily life, they create new words and conceptual frameworks that define our contemporary society. The current investigation investigates Twitter, Facebook, and Reddit SM posts applying textual extraction. The seven-year temporal sample demonstrates significant semantic change caused by society and technology. The analysis notices the importance of new words, phrase meaning evolving, and sentiment changes in SM users’ English usage, proving their adaptability. The growing popularity of phrases like eavesdropping and doom-scrolling indicated how SM and daily life impact. This investigation distinguishes each platform’s unique linguistic features and digital developments by understanding language flow and leading research in the future.

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Section: Literature Reviewmentioning

confidence: 99%

Impact of social media on the evolution of English semantics through linguistic analysis

Shen

2024

Forum Linguist. Stud.

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“…Many of these features are categorized further based on numerous emotional factors. In particular, their study demonstrates that deep RNNs outperform conventional ML algorithms when identifying emotions in music based on instrument categories, which is a key finding in the relevant literature.The need to give piano students a way to evaluate their playing performance is highlighted, so they can get helpful feedback [24][25][26][27] . The realtime recognition of single notes and the non-real-time recognition of multiple notes are both handled by this method.…”

Section: Literature Reviewmentioning

confidence: 99%

Application and algorithm optimization of music emotion recognition in piano performance evaluation

Zhang,

Cai,

Zhang

2024

Environ. Soc. Psychol.

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In the current research, we integrate distinct learning modalities—Curriculum Learning (CL) and Reinforcement Learning (RL)—in an attempt to develop and optimize Music Emotion Recognition (MER) in piano performance. Classical approaches have never been successful when applied in the field of determining the degree of emotion in the music of the piano, owing to the substantial complexity required. Addressing this particular issue is the primary motivation for the present endeavour. In an approach that’s comparable to how human beings acquire information, it trains the RL agent CL in phases; such an approach improves the student’s learning model in understanding the diverse emotions expressed by musical compositions. A higher rating of performance can be achieved after learning the model to recognize emotions more effectively and precisely. A set of piano melodies with emotional content notes has been included in the EMOPIA repository for use when conducting the process of evaluation. In order to benchmark the proposed approach with different models, parameters including R2, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were deployed. Studies indicate that the recommended approach accurately recognizes the emotions expressed by piano-playing music. In challenging tasks like MER, the significance of implementing the CL paradigm with the RL has been emphasized using the outcomes mentioned earlier.

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“…At this point, all of these subjects are calculated by applying a hierarchical Bayesian process, in which every sentence in the document can be represented as a combined collection of a number comprising various subjects [19,20] . As a result of its ability to successfully analyze a significant quantity of unlabeled textual data, this model is a highly beneficial tool for ML [21][22][23][24][25] . This research project uses LDA to find sentimentladen topics from qualitative data for evaluating CTs' MH and JC feedback [26][27][28][29][30] .…”

Section: Introductionmentioning

confidence: 99%

Relationship between mental health and job competence of college teachers: Application of sentiment analysis algorithm

Wu,

Zhuo

2024

Environ. Soc. Psychol.

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In systematically collecting and investigating data related to the teaching field, the association between teachers’ Mental Health (MH) and Job Competence (JC) is an important field that has not been systematically studied. Understanding this association is vital due to its direct impact on the Quality of Education (QoE) and its standard educational system. Due to problems in evaluating all qualitative data, such as Open-Ended Survey Feedback (OESF), traditional models frequently find it challenging to epitomize intricate relations accurately. This article reports on these tasks by introducing a Mixed-Method Approach (MMA). The research study was directed through online learning across numerous higher education institutions in three provinces of China, and a combination analysis of quantitative study data using qualitative Sentiment Analysis (SA) was recommended. The new thing about the method is that it suggests an algorithm based on Latent Dirichlet Allocation (LDA) to SA that lets all of the qualitative data from the OESF questions be studied. This algorithm suggests a more philosophical knowledge of teachers’ MH and its association with their specialized skills overall OESF. The study’s results represent a significant insight into the dynamics between MH and JC related to College Teachers (CT). It highlights how every feature impacts others and the prerequisites for educational strategies supporting teacher well-being. By overcoming boundaries in existing models, the proposed work contributes to a broader and better knowledge of teacher well-being, its impact on educational quality, and the potential for SA in educational research. We compared how well three classifiers—Naïve Bayes (NB), Support Vector Machine (SVM), and Linear Regression (LR)—performed on six topics that were chosen for this SA research. The performance analysis for evaluation is accuracy, precision, recall, and F1-score. The reliability of the OESF measures was confirmed with Cronbach’s alpha values signifying high internal consistency: 0.85 for JC, 0.88 for MH status, and 0.82 for innovative teaching ability.

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Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage

Cited by 10 publications

References 27 publications

Impact of social media on the evolution of English semantics through linguistic analysis

Impact of social media on the evolution of English semantics through linguistic analysis

Application and algorithm optimization of music emotion recognition in piano performance evaluation

Relationship between mental health and job competence of college teachers: Application of sentiment analysis algorithm

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