An Enhanced CNN-2D for Audio-Visual Emotion Recognition (AVER) Using ADAM Optimizer

Indira, D. N. V. S. L. S.

doi:10.17762/turcomat.v12i5.2030

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Nowadays, the recognition of handwritten digits is a dynamic area of exploration within the field of handwriting recognition. Numerous systems for recognizing handwritten digits have been put forth recently to meet the requirements of practical applications, emphasizing the need for high accuracy and reliability in recognition [1]. Handwriting digit recognition is the method that utilizes a computer or machine learning model to recognize and interpret handwritten digits, typically in the range of 0 to 9.…”

Section: Introductionmentioning

confidence: 99%

KurdSet Handwritten Digits Recognition Based on Different Convolutional Neural Networks Models

Hasen Ali,

Abdulrazzaq

2024

TEM Journal

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Recognition of handwritten digits has garnered significant interest among researchers in the domain of recognizing pattern. This interest stems from the recognition's relevance in various real-life applications, including reading financial checks and official documents, which has remained a persistent obstacle. To address this challenge, researchers have developed numerous algorithms focusing on recognizing handwritten digits across different human languages. This paper presents a new Kurdish Handwritten dataset, consisting of Kurdish characters, digits, texts, and symbols. The dataset consists of 1560 participants, encompassing a broad and varied group. It serves as the primary dataset for training and evaluating algorithms in Kurdish digit recognition. We used Kurdish dataset named (KurdSet) and Arabic dataset for handwritten recognition, which holds 70,000 images of Arabic digits that were written by 700 various participants. Additionally, various models are utilized in the study, including ResNet50, DenseNet121, MobileNet, and a custom CNN (convolutional neural network). Additionally, the models' effectiveness was assessed through the examination of test accuracy, which measures the percentage of correctly classified digits in the evaluation phase. ResNet50 also performs exceptionally well that achieved test accuracy 99.67%, indicating its All models exhibit good performance, DenseNet121 and the Custom CNN Model demonstrate the highest test accuracy of 99.73%, highlighting their superior performance. capabilities in capturing relevant features. Despite its accuracy, MobileNet still exhibits good recognition capability with a test accuracy 99.54%.

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Section: Introductionmentioning

confidence: 99%

KurdSet Handwritten Digits Recognition Based on Different Convolutional Neural Networks Models

Hasen Ali,

Abdulrazzaq

2024

TEM Journal

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A Facial and Vocal Expression Based Comprehensive Framework for Real-Time Student Stress Monitoring in an IoT-Fog-Cloud Environment

et al. 2022

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In this era of digital and modern education, the existence of psychological stress on students cannot be denied. The surplus aggregation of the stress may lead to different problems like a decline in student grade (performance), an increase of violence in behavior, and even more extreme cases. The advent of Information Communication and Technology (ICT) and its tools opened the doors to innovations that facilitate interactions among things and humans. In this utilization, the paper proposes a novel, IoT-aware student-centric stress monitoring and real-time alert generating framework to predict student stress index in a particular context. In elaboration, we respectively used extended VGG16, Bidirectional Long Short Term Memory network (Bi -LSTM), and Multinomial Naïve Bayes techniques to generate the scores of emotions from student facial expressions, speech pitch, and content of student speech at the cloud layer. Specifically, the model aims to classify the stress events as normal or abnormal on basis of the overall emotion of the students' physiological data readings. The activation of the abnormal event in case of higher values for negative emotions like stress, fear, sadness, disgust, etc.; a stern alert is sent to the student, coordinators, and caretakers. This proposed framework will ultimately be a great tool that will support the education institutions, students, their parents, and guardians to get a real-time alert on students' overall emotions. The prior knowledge of stress accumulated on the mind of the student will help in overcoming major problems of student dropout, decrease student academic performance, and tackle the stress situation that may lead to the student attempting suicide.

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Machine Learning for Enhanced Student Learning and Engagement in Interactive Classrooms

Saini,

Dhyani,

Sharma

et al. 2024

2024 2nd International Conference on Advancement in Computation &Amp;amp; Computer Technologies (InCACCT)

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An Enhanced CNN-2D for Audio-Visual Emotion Recognition (AVER) Using ADAM Optimizer

Cited by 4 publications

References 16 publications

KurdSet Handwritten Digits Recognition Based on Different Convolutional Neural Networks Models

KurdSet Handwritten Digits Recognition Based on Different Convolutional Neural Networks Models

A Facial and Vocal Expression Based Comprehensive Framework for Real-Time Student Stress Monitoring in an IoT-Fog-Cloud Environment

Machine Learning for Enhanced Student Learning and Engagement in Interactive Classrooms

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