Students’ affective content analysis in smart classroom environment using deep learning techniques

Gupta, Sujit Kumar; Ashwin, T. S.; Guddeti, Ram Mohana Reddy

doi:10.1007/s11042-019-7651-z

Cited by 65 publications

(36 citation statements)

References 28 publications

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“…A deep learning architecture theme can be built with data mining proces Table 16 was developed from the network in Figure 18 Augmented reality, Android and Face recognition are the key drivers of the Deep learning architecture, which occurred on 30%, 25%, and 20% of related manuscripts to Deep learning architecture. Deep learning architecture depends on features such as a sliding window filter used as an incremental database divided into several partitions; accuracy metrics augmented reality, face recognition and android devices [17,134,135]. The application of deep learning architecture is evident in face recognition on campuses.…”

Section: Deep Learning Architecturementioning

confidence: 99%

“…Data mining supports harnessing analysis with deep learning architecture of neural networks for understanding campus end-user behaviour and requirements [3,17,134,135,[141][142][143]. Continuous improvement of smart campus infrastructure produces opportunities for research into smart campus applications, IT infrastructure, network, management, and applications [3,144,145].…”

Section: Implications On Continuous Improvement Of Smart Campus Infrastructurementioning

confidence: 99%

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Infrastructure Elements for Smart Campuses: A Bibliometric Analysis

et al. 2021

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Sustainable development can be attained at a microlevel and having smart campuses around the world presents an opportunity to achieve city-wide smartness. In the process of attaining smartness on campuses, the elements requiring attention must be investigated. There are many publications on smart campuses, and this investigation used the bibliometric analysis method to identify such publications produced over the last decade. A matrix of 578 nodes and 3217 edges was developed from 285 publications on smart campus construction and procurement. Fifteen cluster themes were produced from the bibliometric analysis. The findings revealed that China contributed 48.4% of all published articles on the smart campus. The findings presented a framework from the cluster themes under the four broad infrastructure areas of building construction or repurposing, technology and IT network, continuous improvement, and smart learning and teaching management. The implications of the findings identified that IT project management, traditional procurement strategy, and standard forms of contracts such as the New Engineering Contract (NEC) and the Joint Contract Tribunal (JCT) are applicable in the procurement of smart cities.

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Section: Deep Learning Architecturementioning

confidence: 99%

Section: Implications On Continuous Improvement Of Smart Campus Infrastructurementioning

confidence: 99%

Infrastructure Elements for Smart Campuses: A Bibliometric Analysis

et al. 2021

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“…This is evident in the Smart Campus project reportage in extant literature. Examples of such projects include the development of an anytime-anywhere learning within a Smart Campus environment [46], Smart parking [11,47,48], frameworks for modeling movements on a Smart Campus [49], development of platforms for energy management and optimization on campuses [50][51][52], dynamic timetabling systems [53], the use of apps for location directions and information dissemination purposes [54], real-time space utilization measurement [55], development of a context-aware Smart classroom [56][57][58][59], and the use of digital platforms for IoT-based disaster management [60].…”

Section: Characteristics Of a Smart Campusmentioning

confidence: 99%

Facilitating Successful Smart Campus Transitions: A Systems Thinking-SWOT Analysis Approach

et al. 2021

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An identification of strengths, weakness, opportunities, and threats (SWOT) factors remains imperative for enabling a successful Smart Campus transition. The absence of a structured approach for analyzing the relationships between these SWOT factors and the influence thereof on Smart Campus transitions negate effective implementation. This study leverages a systems thinking approach to bridge this gap. Data were collected through a stakeholder workshop within a University of Technology case study and analyzed using qualitative content analysis (QCA). This resulted in the establishment of SWOT factors affecting Smart Campus transitions. Systems thinking was utilized to analyze the relationships between these SWOT factors resulting in a causal loop diagram (CLD) highlighting extant interrelationships. A panel of experts drawn from the United Kingdom, New Zealand, and South Africa validated the relationships between the SWOT factors as elucidated in the CLD. Subsequently, a Smart Campus transition framework predicated on the CLD archetypes was developed. The framework provided a holistic approach to understanding the interrelationships between various SWOT factors influencing Smart Campus transitions. This framework remains a valuable tool for facilitating optimal strategic planning and management approaches by policy makers, academics, and implementers within the global Higher Education Institution (HEI) landscape for managing successful Smart Campus transition at the South African University of Technology (SAUoT) and beyond.

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“…By detecting faces using a webbased student attendance management system together with XAMPP and MySQL for CNN and k-NN classifier [21]. Student performances were studied within the classroom for 350 students with 71 percent precision on the data collection of the Gold Standard Report than Cohen Kappa on non-verbal signs [22]. To study the affective states of the student on the e-learning environment with 83 percent, 76 percent accuracy on detection and classification, based on spontaneous and posed data sets [23].…”

Section: Deep Learning-based Intelligent Classroom Experiencesmentioning

confidence: 99%

Design and Implementation of Intelligent Classroom Framework Through Light-Weight Neural Networks Based on Multimodal Sensor Data Fusion Approach

Murugesan¹,

Chettiar²

2021

RIA

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Intelligent classrooms are becoming famous. Multimodal sensor data fusion technique, where data generated from different sensors are fused to derive at some valuable insights from the classroom settings. In this paper, the proposed framework model tries to enable intelligence in a traditional Classroom Environment by experimenting on modules such as Deep Learning based Face recognition systems, Interactive Smart Mirror Assistant, Indoor Classroom Air quality monitors. Sensor hub (Jedi One) helps to visualize and analyse streaming data in real-time. Based on the proposed framework design, experimentations are carried out. The accuracy achieved in the Face Recognition System of 71% has to be increased with 80-90% by finetuning the parameters. In future, Interactive dashboards can be activated via PowerBI or Excel worksheets. Based on the questionnaire study & responses from the participants on AI & IoT systems inside the classrooms, more than 50% responded positively to support the usage of these technologies in a Classroom Environment. The future classrooms will be (DLeIC) Deep Learning enabled IoT Classrooms to lift the educational space into a new dimension. Incorporating Deep Learning on IoT systems can be a savvy and fruitful path to collaborate with generations to come.

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Students’ affective content analysis in smart classroom environment using deep learning techniques

Cited by 65 publications

References 28 publications

Infrastructure Elements for Smart Campuses: A Bibliometric Analysis

Infrastructure Elements for Smart Campuses: A Bibliometric Analysis

Facilitating Successful Smart Campus Transitions: A Systems Thinking-SWOT Analysis Approach

Design and Implementation of Intelligent Classroom Framework Through Light-Weight Neural Networks Based on Multimodal Sensor Data Fusion Approach

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