Classifying Emotional Engagement in Online Learning Via Deep Learning Architecture (original) (raw)

The world has seen a phenomenal rise in online learning over the past decade, with universities shifting courses to online modes, MOOCs(Massive Open Online Course) emerging and laptop and tab-based initiatives being extensively promoted. However, educators face significant challenges in analyzing learning environments due to issues like lack of in-person cues, small video size, etc. To address these challenges, it is crucial to analyze the engagement levels of online classes. Out of the various subcategories of engagement, emotional engagement is one that is overlooked, but integral to analysis and deterministic in its approach. In response, we developed a deep learning architecture to analyze emotional engagement in online classes. Our method utilizes a ResNet50-based algorithm, refined through experimentation with various techniques such as transfer learning, optimizers, and pre-trained weights. The model adds a unique layer to the analysis of different algorithms used for engagement detection in academia while also achieving stellar rates of 81.34% validation accuracy and 81.04% training accuracy. Unlike other models, our approach employs high-quality image data for training, ensuring more reliable results. Moreover, we constructed a novel framework for applying emotional engagement to real-world scenarios, thus bridging the pre-existing gap between implementation and academia. The integration of this technology into online learning has immense potential, and can bring with it a shift in the quality of education. By fostering a safe and healthy learning space for every student, we can significantly enhance the effectiveness of online education systems.

Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for Analyzing Product Sentiments in Online Business in Nigeria

In today's e-commerce in Nigeria, customers access online stores to browse through and place orders for products or services via the internet on their devices while some are skeptical due to the experiences from what I ordered versus what I got syndrome. Though this method of business has flourished to an extent, it greatly faces a crucial challenge in unravelling consumer's sentiments particularly in the realm of product reviews. This deficiency inhibits most e-commerce platforms in Nigeria from gaining effective sensitivity into users' preferences, thus, limiting their ability to boost their product recommendations and, understand and improve customers' experiences. This research aims to bridge this gap by developing a sentiment analyzer of product in the e-commerce domain using Natural language processing and machine learning approach. The model will analyze the customers' reviews based on positive or negative. The experimental data was collected from kaggle.com. Stemming and lemmatization were approaches used for cleaning the collected data. Features were extracted and transformed using CountVectorizer. Gaussian Naïve Bayes classifier was used as the machine learning technique. The model's performance was evaluated and it returned 90% of accuracy, hence, an efficient and reliable model for product review sentiment analysis is developed.

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Emotional Intelligence (EI) Quotient-based Psychological Contract Impact on Employee's Satisfaction

Research relates to investigating association of emotional intelligence with employee's satisfaction, whether it safeguard the relationship between employee and employer, whether it effects on employee's satisfaction, fairness perception, promotion and privileges. In hypothesis 1, emotional intelligence effects on employee's satisfaction based psychological contract. In hypothesis 2, emotional intelligence effects on fairness perception based psychological contract. In hypothesis 3, emotional intelligence effects on promotion and privileges based psychological contract. Quantitative method applied in this study and distributed questionnaires for data collecting in four different mobile companies, 100 from private sector and 100 from public sector.

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Creative Authenticity: A Framework for Supporting the Student Self in Craft Education

This article introduces a pedagogical approach in design education referenced as creative authenticity. Creative authenticity is defined as an ongoing process of learning to create through intrinsically motivated, self-aware and self-affirming actions and rationales. The concept is grounded in Constructivist learning theory, Postmodernist views of pluralism and cultural position, Anthony Giddens' theory of reflexive identities, and scholarship on intrinsic motivation in learning. This ideology seeks to personalize the learning experience for each student in ways that are meaningful to their person, not just useful to the design industry, at large. This conversation proposes four samples of methodology by which to infuse creative authenticity into curriculum as a starting point for shaking off implicit biases; focusing on student learning and growth; initiating meaningful and empowering discussions; and redefining success through collaborative and participatory educational design. This work promotes teaching with creative authenticity as a foundation to help students realize their strengths through their ever-evolving identities. In a broader context, authenticity in education supports marginalized groups to see themselves, their histories and their experiences authentically reflected in their education and work.

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Deep Learning Neural Networks in the Cloud

Deep Neural Networks (DNNs) are currently used in a wide range of critical real-world applications as machine learning technology. Due to the high number of parameters that make up DNNs, learning and prediction tasks require millions of floating-point operations (FLOPs). Implementing DNNs into a cloud computing system with centralized servers and data storage subsystems equipped with highspeed and high-performance computing capabilities is a more effective strategy. This research presents an updated analysis of the most recent DNNs used in cloud computing. It highlights the necessity of cloud computing while presenting and debating numerous DNN complexity issues related to various architectures. Additionally, it goes into their intricacies and offers a thorough analysis of several cloud computing platforms for DNN deployment. Additionally, it examines the DNN applications that are already running on cloud computing platforms to highlight the advantages of using cloud computing for DNNs. The study highlights the difficulties associated with implementing DNNs in cloud computing systems and provides suggestions for improving both current and future deployments.

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Facial Emotion Recognition using a Modified Deep Convolutional Neural Network Based on the Concatenation of XCEPTION and RESNET50 V2

SSRG International Journal of Electrical and Electronics Engineering ( IJEEE ), 2023

Facial emotion recognition has gained significant attention in modern years due to its wide applications in numerous fields, including human-computer interaction, market research and healthcare. This research focuses on improving facial emotion recognition accuracy by proposing a modified deep learning method based on the concatenation of Xception and ResNet50 architectures. The proposed approach aims to leverage the strengths of both Xception and ResNet50 networks to enhance facial expression representation and classification. Xception is known for its efficient feature extraction capabilities, while ResNet50 excels in capturing deeper and more complex patterns. By combining these architectures, the modified deep learning model can achieve higher emotion recognition accuracy. The research involves several stages. First, a large dataset of facial expressions is collected and preprocessed. The facial images are then fed into the modified deep-learning model, where feature extraction and classification occur. The model learns to recognize patterns and associations between facial expressions and specific emotions through a supervised learning process. Six distinct pre-trained DCNN models (ALEXNET, INCEPTIONV3, RESNET 50, VGG 16, XCEPTION and the concatenation of XCEPTION and RESNET50 V2) are used to validate the proposed system and with well-known datasets of FER2013, KDEF, CK+ JAFFE and with newly created custom Dataset-1 of 9K facial images. The proposed novel technique showed astounding accuracy, with a validation accuracy of 97.58% for a Softmax classifier, and it also recognized XCEPTION-RESNET V2 as the best network, with training and validation accuracy of 99.99% and 90%, respectively.

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A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

Electronics

Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest...

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An Overview of Supervised Machine Learning Paradigms and their Classifiers

Artificial Intelligence (AI) is the theory and development of computer systems capable of performing complex tasks that historically requires human intelligence such as recognizing speech, making decisions and identifying patterns. These tasks cannot be accomplished without the ability of the systems to learn. Machine learning is the ability of machines to learn from their past experiences. Just like humans, when machines learn under supervision, it is termed supervised learning. In this work, an in-depth knowledge on machine learning was expounded. Relevant literatures were reviewed with the aim of presenting the different types of supervised machine learning paradigms, their categories and classifiers.

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The Influence of Emotional Intelligence on Creativity, The Mediating Role of Employee Attitudes: Analysis of Mellat Holding in Kurdistan

The recent findings revealed that emotional intelligence has a vital influence in creating creativity, but, so far little effort has been done to verify this. Therefore, the current research aimed to examine the impact of emotional intelligence on creating with the mediation role of employee attitudes at selected at Mellat Holding in Kurdistan region of Iraq. A quantitative research method was applied to measure the current study. A random sampling method was applied, 150 questionnaires distributed but only 121 questionnaires were received. The study used four dimensions of emotional intelligence (social awareness, self-management, relationship management, self-awareness) as independent variable, also employee attitude as a mediator and creativity as dependent variable. The findings revealed that emotional intelligence have direct and indirect significant and positive influence on creativity at Mellat Holding. Furthermore, the study suggested to include employee engagement and employee commitment as future studies.

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Effects of Different Datasets, Models, Face-parts on Accuracy and Performance of Intelligent Facial Expression Recognition Systems

International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING, 2024

Facial expression recognition is a crucial area of study in the field of computer vision. Research on nonverbal communication has shown that a significant amount of deliberate information is sent via facial expressions. Facial expression recognition is a crucial field in computer vision that deals with the significant impact of nonverbal communication. Expression recognition has lately been extensively used in the medical and advertising sectors. Difficulties in Facial Emotion Recognition. Facial emotion recognition is a technique that examines facial expressions in static photos and videos to uncover information about an individual's emotional state. The intricacy of facial expressions, the versatile use of the technology in any setting, and the incorporation of emerging technologies like artificial intelligence pose substantial privacy hazards. Facial expressions serve as non-verbal cues, offering indications of human emotions. Deciphering emotional expressions has been a focal point of study in psychology for many years. This study will examine several prior studies that have undertaken comprehensive facial analysis, including both total and partial face recognition, to identify expressions and emotions. The datasets and models used in previous studies, as well as the findings gained, show that employing the whole face yields more accuracy compared to using specific face-parts, which result in lower accuracy ratios. However, emotional identification often does not rely only on the whole face, since it is not always feasible to have the full face available. Contemporary research is now prioritising the identification of facial expressions based on certain facial features. Efficient deep learning algorithms, particularly the CNN algorithm, can do this task.

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