Set Of Pictures Research Articles

Phonological planning in Cantonese–English bilingual speech production

Purpose: Findings from previous speech production research suggest that the nature of phonological planning units is language-specific, with phonemes as the planning units in Dutch and English but syllables in Cantonese and Mandarin Chinese. However, little is known about how multilingual speakers possessing languages with distinctive phonological planning units plan for their speech. This study was conducted to investigate the roles of syllable and phoneme in Cantonese (L1) and English (L2) production among Cantonese–English bilinguals. Design: A picture version of the form-preparation task was employed, where participants named aloud (in their L1 and L2) small sets of pictures repeatedly. The names of the pictures in a set either shared the same word-initial syllable, word-initial phoneme, or were unrelated. Participants’ L1 and L2 proficiencies were measured. Data and analysis: 53 Cantonese–English bilinguals participated. The naming latency data were submitted for linear mixed effects modeling analyses. Findings: Significant priming effects on naming latency, relative to an unrelated control, were found when the response words shared the same word-initial syllable but null effects were found when they shared merely the same word-initial phoneme in both L1 and L2 production. Critically, in L2 English production, a significant negative association was observed between the size of syllable priming and the participant’s English vocabulary size. Furthermore, significant English phonemic priming was observed among the participants higher in spoken English proficiency but not among the lower proficiency counterparts. Originality: This study provides clear evidence showing the multifaceted effects of L2 proficiency on L2 phonological planning using a picture naming task. Significance: The present findings indicate the significant impact of L1 on L2 phonological planning. Yet, this L1-to-L2 influence would be modulated in different ways by the different aspects of one’s L2 proficiency.

Exploring quantitative measures in metacognition of emotion

Metacognition of emotion (meta-emotion) refers to the ability to evaluate and identify one’s emotional feelings. No previous study has defined and measured this construct through objective and quantitative procedures. We established a reliable method to measure meta-emotion. With a two-interval forced-choice procedure, participants selected which of two pictures elicited stronger positive emotion; via the Law of Comparative Judgment, their responses were used to compute individual psychological distances for the emotional responses triggered by the pictures. Then, participants were asked to judge whether a pre-exposed picture induced a stronger positive emotion than the median of that elicited by the whole picture set, followed by a confidence rating. By utilizing each individual’s psychological distance, the correctness of a participant’s emotional experience was quantified by dʹ, and meta-emotion was quantified using meta-dʹ, M-ratio, and M-diff as indices of metacognitive sensitivity and efficiency based on Signal-Detection Theory. Test–retest reliabilities, validated by Spearman correlation, were observed in meta-dʹ, M-ratio, and marginally with M-diff, suggesting the stability of meta-emotion in the current design. This study unveils a validated procedure to quantify meta-emotion, extendable for assessing metacognition of other subjective feelings. Nevertheless, caution is warranted in interpretation, as the measured processes may be influenced by non-metacognitive factors.

A Literature Survey on Completely Blind Image Quality Evaluator Feature-Enriched

Existing blind image quality assessment (BIQA) technique in which are principally opinion-aware. They learn deterioration models from training photos with associated human subjective scores to predict the sensory activity quality of take a look at pictures. Such opinion-aware methods, however, need a bulky amount of training example with associated human prejudiced achieve moreover of a variety of deformation category. The BIQA representation find out through opinion-aware strategies typically have weak generalization capability, herewith limiting their usability in observe. By comparison, opinion-unaware strategies don't require human prejudiced achieve for training, and then have superior possible designed for good quality simplification ability. Unluckily, to the present point no opinion-unaware BIQA technique has shown systematically better quality prediction accuracy than the opinion-aware strategies. Here, we tend to aim to develop an opinionunaware BIQA technique that may compete with, and maybe exceed, the present opinion-aware methods. By integration the options of ordinary picture information consequent as of multiple cues, we have an affinity to discover a multivariate Gaussian structure of representation patches from a set of perfect natural pictures. using the learned multivariate Gaussian form, a Bhattacharyya-like distance is employed to measure the standard of every image patch, and then a generally value score is find by average pooling. The projected BIQA technique doesn't want any distorted sample images or subjective class achieve for training, yet extensive experiment show its better quality-prediction presentation to the situation of the art opinion-aware BIQA strategies.

Evaluation of different median filters for image processing tasks

Impulsive noise is captured at the acquisition stage of digital image processing. It is an important step in the pre-processing process which is used to improve the image quality and make it more informative. There have been several proposed image-denoising algorithms, each with advantages and disadvantages. This work gives a thorough investigation of the median filter and its various variations for the reduction or elimination of impulsive noise in grey scale images. In terms of usefulness and comparative performance, Standard Median Filter, Adaptive Median Filter and Modified Median Filter are contrasted. On a set of pictures, thorough MATLAB-based simulations have been run to assess the performance of the current algorithms. Peak Signal to Noise Ratio was employed to compare performance in this case (PSNR). In terms of relative statistical performance, the Modified Median Filter (MMF) is the best.

A Literature Survey on Completely Blind Image Quality Evaluator Feature-Enriched

Existing blind image quality assessment (BIQA) technique in which are principally opinion-aware. They learn deterioration models from training photos with associated human subjective scores to predict the sensory activity quality of take a look at pictures. Such opinion-aware methods, however, need a bulky amount of training example with associated human prejudiced achieve moreover of a variety of deformation category. The BIQA representation find out through opinion-aware strategies typically have weak generalization capability, herewith limiting their usability in observe. By comparison, opinion-unaware strategies don't require human prejudiced achieve for training, and then have superior possible designed for good quality simplification ability. Unluckily, to the present point no opinion-unaware BIQA technique has shown systematically better quality prediction accuracy than the opinion-aware strategies. Here, we tend to aim to develop an opinionunaware BIQA technique that may compete with, and maybe exceed, the present opinion-aware methods. By integration the options of ordinary picture information consequent as of multiple cues, we have an affinity to discover a multivariate Gaussian structure of representation patches from a set of perfect natural pictures. using the learned multivariate Gaussian form, a Bhattacharyya-like distance is employed to measure the standard of every image patch, and then a generally value score is find by average pooling. The projected BIQA technique doesn't want any distorted sample images or subjective class achieve for training, yet extensive experiment show its better quality-prediction presentation to the situation of the art opinion-aware BIQA strategies.

Development and Validation of a Digital Photographic Atlas of Argentine Foods

Introduction. Before the Second National Health and Nutrition Survey in Argentina, it was necessary to create a digital visual tool to help participants in the quantification of intake. This study describes the development of a Digital Photographic Atlas of Argentinean Foods (AFDAA) and evaluates its accuracy in visually estimating the amounts of foods consumed in Argentina. Methods. A total of 292 photographs of food/dishes were taken in standardized conditions and classified into 103 series according to food group. Thirty series were selected for validation. Adults ≥ 18 years of age were invited to participate in three validation sessions that were conducted at “blinded” between 2016 and 2018. During each session, the participant´s ability to visually relate a real amount of food presented on a plate to an amount depicted in a photograph series was assessed. The difference between the participant’s perception of the weight of foods/dishes in photographs and the real weight was expressed as a percentage. The average percentage difference was estimated, and 95% confidence intervals were used. When ≥ 50% of the differences were outside the ±30% range, the set of pictures was removed from the final version. Results. This free-to-use digital Atlas is a valuable tool that can be employed in future dietary surveys to quantify the consumption of foods similar to those depicted in the images. Conclusion. The degree of BMI tends to increase the level of sleepiness. Funding: This research received financial support from UNICEF and the Universidad Nacional de La Matanza (Grant C2SAL012).

RESDEN: A Novel Deep Unified Model for Face Recognition System

The Face Recognition technology plays a significant role in the field of Computer Vision in contemporary times. The research article is centered on a Facial attendance system that utilizes a deep learning technique to recognize face photos. To execute face identification and classification via the use of deep learning processes, many Convolutional Neural Network (CNN) models are taken into account. Previous studies have mostly focused on either the ResNet or DenseNet-based convolutional neural network model. The present research utilizes the merging of ResNet and DenseNet to propose a hybrid model. The proposed work is expected to provide enhanced efficiency and accuracy. In the training and testing stages of the simulation, considerations are made for both binary and category classifications. The current research focuses on the use of the LFW dataset. The pictures undergo an initial step of the noise reduction process. The evaluation of picture quality is conducted by taking into account metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). After the proposed model has undergone training, it generates photographs of superior quality. Finally, the proposed system incorporates the RESDEN framework, which integrates DenseNet with a noise reduction technique, a segmentation mechanism, and a CNN based on ResNet. A comparative analysis has been conducted to evaluate the accuracy of several filtered picture sets across different convolutional neural network (CNN) models. The simulation results indicate that the suggested model exhibited a good level of performance and accuracy.

Right amygdala and caudate activation patterns predict implicit attitudes toward people with autism spectrum disorders and physical disabilities, respectively.

Attitudes toward people with disabilities tend to be negative, regardless of the visibility of the disorder traits. Individuals with autism spectrum disorder (ASD) experience stigmatization that is rooted in negative attitudes or prejudice toward them because of their social awkwardness. The neural underpinnings of attitudes toward people with disabilities remain unclear. In this study, we focused on implicit attitudes toward people with ASD and physical disabilities, which are more visible than ASD, and investigated whether implicit attitudes were predicted by using neural activity with multivoxel pattern analysis (MVPA) in the prejudice network. Thirty-six, right-handed, Japanese, university students without disabilities participated. Two implicit association tests (IAT) toward people with ASD and physical disabilities revealed negative implicit attitudes. In the MRI scanner, participants performed a one-back task by using the same picture sets of IATs to examine their neural responses toward people with ASD and physical disabilities. According to the MVPA results, activation patterns of the right amygdala and right caudate significantly predicted implicit attitudes toward people with ASD and physical disabilities, respectively. These results suggest that implicit attitudes toward ASD and physical disabilities can be predicted by using neural signals from different regions within the prejudice network.

Photovoice: Visualizing the engineering identity experiences of sophomore students

AbstractBackgroundResearchers have shown that students leave undergraduate engineering programs during the first 2 years. Justifiably, many studies have tried to tackle engineering student persistence and attrition, especially during the first year, and then developed interventions to address the challenges. Although those interventions have improved freshmen retention in some institutions, less has been published on the impacts of these interventions on the sophomore student experience.PurposeTo contribute to the knowledge base about all engineering students, we examined the experiences of sophomore engineering students and explored how these experiences might be related to their identities as engineers.Design/MethodWe conducted this study using photovoice, a methodology in which participants submit photographs to describe their experiences and give recommendations on improving their experiences and resolving their concerns. Participants submitted three sets of pictures (at the beginning, middle, and end of the semester) and participated in focus groups to aid in illuminating their experiences. We analyzed data using thematic analysis.ResultsWe inductively determined three themes: on the frame, out of focus, and prefigures. These themes illustrate the experiences of sophomore engineering students, enabling us to see what interest, competence, and recognition as engineers looked like from their perspectives.ConclusionsParticipants grappled with the tension between their personal, social, and engineering identities. Photovoice empowered them to author and illustrate that they could exist beyond the murky middle.

Reliability and agreement testing of a new automated measurement method to determine facial vitiligo extent using standardized ultraviolet images and a dedicated algorithm.

Facial repigmentation is the primary outcome measure for most vitiligo trials. The Facial Vitiligo Area Scoring Index (F-VASI) score is often chosen as the primary outcome measure to assess the efficacy of treatments for facial vitiligo. Although useful, this scoring system remains subjective and has several limitations. To assess the agreement and reliability of an algorithmic method to measure the percentage depigmentation of vitiligo on the face. We developed a dedicated algorithm called Vitil-IA® to assess depigmentation on standardized facial ultraviolet (UV) pictures. We then conducted a cross-sectional study using the framework of the ERASE trial (NCT04843059) in 22 consecutive patients attending a tertiary care centre for vitiligo. Depigmentation was analysed before any treatment and, for 7 of them, after 3 and 6 months of narrowband UVB treatment combined with 16 mg methylprednisolone, both used twice weekly. Interoperator and interacquisition repeatability measures were assessed for the algorithm. The results of the algorithmic measurement were then compared with the F-VASI and the percentage of depigmented skin scores assessed by 13 raters, including 7 experts in the grading of vitiligo lesions. Thirty-one sets of pictures were analysed with the algorithmic method. Internal validation showed excellent reproducibility, with a variation of < 3%. The percentage of depigmentation assessed by the system showed high agreement with the percentage of depigmentation assessed by raters [mean error (ME) -11.94 and mean absolute error (MAE) 12.71 for the nonexpert group; ME 0.43 and MAE 5.57 for the expert group]. The intraclass correlation coefficient (ICC) for F-VASI was 0.45 [95% confidence interval (CI) 0.29-0.62] and 0.52 (95% CI 0.37-0.68) for nonexperts and experts, respectively. When the results were analysed separately for homogeneous and heterogeneous depigmentation, the ICC for homogeneous depigmentation was 0.47 (95% CI 0.31-0.77) and 0.85 (95% CI 0.72-0.94) for nonexperts and experts, respectively. When grading heterogeneous depigmentation, the ICC was 0.19 (95% CI 0.05-0.43) and 0.38 (95% CI 0.20-0.62) for nonexperts and experts, respectively. We demonstrated that the Vitil-IA algorithm provides a reliable assessment of facial involvement in vitiligo. The study underlines the limitations of the F-VASI score when performed by nonexperts for homogeneous vitiligo depigmentation, and in all raters when depigmentation is heterogeneous.

PEDAGOGICAL POTENTIAL OF A PICTURE TO ENHANCE SPEECH FOR CHILDREN WITH LANGUAGE DISORDERS

The scientist A. Leontyev assures that the language competence is the result of development of continuous use of each language. Only through active communication and active speech activity an understanding about language emerges for the child (Leontyev, 1977). The scientist Glukhov (2005) outlines the skill to improve the language abilities by performing the speech activity in the process of communication. Whereas, one of the most significant factors in the process of mastering the language is a positive emotional background, where the child is encouraged to perceive visible, apparent things, living beings, natural phenomena, to listen and pronounce words which designate them. This also inspires to investigate the pedagogical potential of a picture to enhance speech for children with language disorders.The picture is a significant visual aid for a child not only to be taught but also to have fun for improving the language skills. Therefore, it is worked out a set of pictures as a help for the parents to work with a child at home. In a such way a speech therapist is involving the parents in a teaching process to prolong an educational process with a child. As a result, the parents get a pedagogic experience and the efficient development for a child language.

Experimental Pain Picture System (EPPS): Development and Validation

Pain-related pictures are useful for studying how individuals respond to pain-related stimulation. Such pictures can occasionally be found in databases for affective pictures. However, a validated database specifically for pain-related pictures is not available yet. In 2 experiments (N = 185 and 103, respectively), we developed and validated the Experimental Pain Pictures System (EPPS). In both experiments, negative valence, arousal, and painfulness ratings were compared between neutral-, sad-, and pain-related pictures. The pain-related pictures represented both deep and superficial somatic pain. Across the 2 experiments, pain-related pictures were judged as more negative, arousing, and painful than neutral pictures and more painful than sad pictures. The final EPPS contains 50 pictures of different painful events considered moderately to highly painful by participants. The EPPS is a valuable tool for studying pain-related responses, as it gives researchers a choice among many validated pictures depicting different types of pain, increasing the comparability between studies. PerspectiveThis article presents the validation of the experimental pain pictures system, which consists of a set of pain-related pictures. The experimental pain pictures system is composed of pictures depicting different types of pain. Participants rated all the pictures as being negative, arousing, and painful.

AN ANALYSIS TYPE OF PLOT INCREDIBLES 2 MOVIE

As part of this inquiry, the researcher evaluated the plot type of Incredibles 2, which was released in 2018. This study is to determine the many sorts of storylines that were employed by the writer in the motion picture The Incredibles 2 (also known as “Incredibles 2”), which is also known as “Incredibles 2.” The researcher employed descriptive approaches in order to describe and analyze the data that was obtained for this study. In order to gain a detailed description of the storyline in this movie, the researcher used a qualitative approach, and he or she also used descriptive techniques. The researcher continues by putting Robert Stanton’s notion about the storyline into practice. During the course of this investigation, the researcher came across four unique sets of pictures, namely two picture flashback charts. The researcher may come to the following conclusion after going through all of the information and analyzing it, because the events of the story take place of the data analysis, the author participates that the reader will have a better comprehension of the storyline of the movie Incredibles 2 after reading this research. The author thinks that the study that is offered here will be helpful to other readers as a resource for plot analysis.

The Outcomes of Remote Administration of Combined Aphasia and Apraxia of Speech Treatment: A Single-Subject Experimental Design Study.

This study was designed to examine the outcomes of Combined Aphasia and Apraxia of Speech Treatment (CAAST) administered remotely in terms of acquisition and generalization effects and to compare these effects to previous in-person CAAST studies and Response Elaboration Training (RET)/Modified-Response Elaboration Training (M-RET) benchmarks. Multiple probe designs across participants and behaviors were employed with three speakers with chronic aphasia and apraxia of speech. Correct information units (CIUs) were the primary outcome measure to measure changes in language production. Percent consonants correct (PCC) was used as the secondary outcome measure to evaluate changes in speech sound accuracy. Production of CIUs was compared with existing benchmarks from Bunker et al.'s (2019) meta-analysis of previous RET/M-RET studies. In addition, both CIUs and PCC were compared with the most recent CAAST in-person studies. All participants demonstrated substantial increases in CIUs for treated and untreated picture sets, comparable to outcomes of in-person CAAST administration. These language changes were maintained at posttreatment intervals for all participants. PCC also improved for all participants, with gains in articulatory accuracy being maintained posttreatment. Improvements in CIU production and PCC for all three participants were in keeping with results from Wambaugh et al. (2017). These findings provide additional support for the efficacy of CAAST and indicate that remote administration may be a viable alternative to in-person application. https://doi.org/10.23641/asha.23418635.

The Effect of Age and Task Complexity on the Microstructure of Child Arabic Narratives.

This study examines the development of narrative microstructure elements of productivity, lexical diversity, and syntactic complexity in the oral story production of preschool- and school-age Kuwaiti Arabic-speaking children. It also explores the effects of story task complexity on the target microstructural features. This study employed a cross-sectional research design and enrolled 96 monolingual speakers of Kuwaiti Arabic. Four groups of children aged 4;0-7;11 (years;months) were randomly recruited from public schools across Kuwait. The groups consisted of 22 four-year-olds (Kindergarten 1), 24 five-year-olds (Kindergarten 2), 25 six-year-olds (Grade 1), and 25 seven-year-olds (Grade 2). Two sets of sequential pictures from the Edmonton Narrative Norms Instrument were used to elicit storytelling from all participants: a one-episode story and a more complex three-episode story. The children's stories were analyzed to determine if there were differences in narrative microstructure as a function of age and task complexity. The data indicated that productivity, lexical diversity, and syntactic structures increased with task complexity. The length of communication units, the average mean length of the three longest utterances, and the amount and variety of words in the children's productions were all significantly larger in the more complex story. Only one syntactic structure showed age as well as task effects. Clinical recommendations include adapting the coding scheme to fit Arabic data, using the more complex narrative alone for microstructure analysis, and calculating only a few measures for productivity and syntactic complexity to save time.

Peningkatan Keterampilan Menulis PuisiMelalui Model Picture and Picture Dengan Menggunakan Media GambarPada Kelas V MI Darul Iman

A study has been carried out entitled Improving Poetry Writing Skills Through Picture and Picture Models with Image Media in Class V MI Darul Iman Students. In learning to write poetry using the technique of using picture and picture models with image media. A learning method that invites students to observe a set of pictures in class. The picture-and-picture method is also very useful in learning poetry. The essence of writing poetry is the result of recording events or pictures of interesting objects that are poured through his mind into written language. The picture and picture method here can inspire students to express themselves in poetry, by observing an interesting picture. The research was conducted at MI Darul Iman in class V and this research was carried out in 2 cycles and 4 stages in each cycle namely Planning, Action, Observation, and Reflection. Based on the results of research that has been carried out using the picture and picture model with image media can improve teachers in learning poetry writing skills in class V MI Darul Iman students, it can also increase student activity in learning poetry writing skills and can increase the class average value in the cycle 1 with a score of 66.67 with a completeness percentage of 33.33% increased in cycle 2 to 80.07 with a completeness percentage of 86.66%. There was an increase in student scores of 53.33% from cycle 1 to cycle 2 with Very Good criteria.

Reduced syntactic recursion in spontaneous speech of Parkinson's disease patients

Although characterized as a movement disorder, Parkinson's disease (PD) affects more than just the motor system. Within the heterogenous non-motor symptoms, language impairment is frequent but poorly understood beyond semantic processing.This study investigates the impact of PD on syntactic subordination in spontaneous language production. Fifteen PD patients in ON levodopa status narrated a short story guided by a set of pictures. Thirteen PD patients were also assessed in OFF levodopa status. Narrations were digitally recorded, subsequently transcribed and annotated, making the produced speech accessible to systematical quantitative analysis.Compared to a healthy matched control group, PD patients showed a significant reduction of subordinating structures while the number of non-embedding sentences remained unaffected. No significant effect comparing ON versus OFF levodopa status emerged. Our results suggest a contribution of the basal ganglia to language processing, such as syntactic composition, which, however, does not seem to be dopamine dependent.

Engagement in collaborative writing: Exploring learners’ control of task content and text quality

AbstractTo extend L2 collaborative writing (CW) research that has largely focused on learners’ interactional behaviours and written performance, the current study adopted learner engagement as a theoretical lens to investigate whether different aspects of learner engagement in CW (i.e., cognitive, emotional and social) are affected by learners’ control of task content and whether they predict text quality. Thirty ESL learners performed two CW tasks, with the first task giving learners all pictures to co‐construct a story in writing whereas in the second task they were given half of the picture set and thus had to self‐produce the content for the rest of the story. Learner engagement during CW was gauged by multiple measures (e.g., LREs, idea units, instances of responsiveness, and self‐rated emotions). Post‐task interviews were conducted to supplement the quantitative results about the impact of the control of task content on learner engagement. Discourse analysis was applied to examine the linguistic aspects of the joint‐written text. Learner engagement scores were compared across two task conditions and entered into regression models to examine their association with the text quality. Results revealed that learners showed and reported higher cognitive, social, and emotional engagement in CW when they had greater control over the task content than when provided with all content. LREs, an indicator of cognitive engagement, were found to predict the texts’ accuracy. Results are discussed in terms of the design of CW tasks and the role of learner engagement in text quality.

Ocean Acidification as a Hyperobject: Mediating Acidic Milieus in the Anthropocene

Through the usage of Timothy Morton’s hyperobjects (2013) as a heuristic, this essay aims to portray how Ocean Acidification can be read as a hyperobject affecting tropical seawaters and beyond. Furthermore, it illustrates how the arts and humanities, through their hermeneutical gaze, might help us grasp Ocean Acidification as a hyperobject and the wide array of other objects that act upon each other in such acidic oceanic waters. In this task, the article will close-read the Underwater Woman set of pictures by Christine Ren (2018) understanding the interpretation of art as a tool to reconnect cognition and emotion to move from the understanding of a crisis to the feeling of such crisis. Finally, it aims to shed light upon the implications arising from considering Ocean Acidification as a hyperobject. By connecting the theoretical, visual and political in the same narrative, this essay highlights the transformative potential of interpretation and thinking through hyperobjects. With this, the challenges of the Anthropocene are put at the forefront, situating specific events and problematics in a planetary scale.

Enhanced Emotion Recognition in Videos: A Convolutional Neural Network Strategy for Human Facial Expression Detection and Classification

The human face is essential in conveying emotions, as facial expressions serve as effective, natural, and universal indicators of emotional states. Automated emotion recognition has garnered increasing interest due to its potential applications in various fields, such as human-computer interaction, machine learning, robotic control, and driver emotional state monitoring. With artificial intelligence and computational power advancements, visual emotion recognition has become a prominent research area. Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. To address these issues, we propose a comprehensive CNN-based model for real-time detection and classification of five primary emotions: anger, happiness, neutrality, sadness, and surprise. We employ the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV) video dataset, extracting image frames from the video samples. Image processing techniques such as histogram equalization, color conversion, cropping, and resizing are applied to the frames before labeling. The Viola-Jones algorithm is then used for face detection on the processed grayscale images. We develop and train a CNN on the processed image data, implementing dropout, batch normalization, and L2 regularization to reduce overfitting. The ideal hyperparameters are determined through trial and error, and the model's performance is evaluated. The proposed model achieves a recognition accuracy of 99.38%, with the confusion matrix, recall, precision, F1 score, and processing time further quantifying its performance characteristics. The model's generalization performance is assessed using images from the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) and Extended Cohn-Kanade Database (CK+) datasets. The results demonstrate the efficiency and usability of our proposed approach, contributing valuable insights into real-time visual emotion recognition.