Visual Emotion-Aware Cloud Localization User Experience Framework Based on Mobile Location Services

The traditional multiple reserves and resounding costs of increase of the corporal volume can be replaced by a distributed file model of divided data employing algorithms, multidimensional integrity, and resilience to complete losses of the storage facilities. The model's created duplicated data enables you to recover some of the split components' lost functionality. A rapid shift in the appraisal and application of knowledge is also required by increasing information and communication technologies. As a result, modern learning methodologies, approaches, and technologies are being developed. This paper describes how to build an automated instructional and analytical complex using semantic information. It has been established what is needed for such an educational and technological complex. Tools built on a semantic network are represented in formal and structural models. Since it was created using a thorough understanding of the topic, the model is intended to facilitate students' perception of the many educational and technical complexities and to enhance the teacher's work.

Section: Related Workmentioning

confidence: 99%

Multidimensional Parity Algorithms to Escalate the Security of Intelligent Mobile Models in Education

Vidhya¹,

Padmapriya²,

Selvakumar³

et al. 2023

“…The authors of [15] proposed a framework that offers a mobile application tailored to the user's visual emotions. It processes the collected emotional data of a mobile user by wireless sensor [16] on the cloud and analyzes them immediately.…”

Section: Related Workmentioning

confidence: 99%

Improved Methods for Automatic Facial Expression Recognition

Echoukairi¹,

Ghmary²,

Ziani³

et al. 2023

Facial expressions constitute one of the most effective and instinctive methods that allow people to communicate their emotions and intentions. In this context, the both Machine Learning (ML) and Convolutional Neural Networks (CNNs) have been used for emotion recognition. Efficient recognition systems are required for good human-computer interaction. However, facial expression recognition is related to several methods that impact the performance of facial recognition systems. In this paper, we demonstrate a state-of-the-art of 65% accuracy on the FER2013 dataset by leveraging numerous techniques from recent research and we also proposed some new methods for improving accuracy by combining CNN architectures such as VGG-16 and Resnet-50 with auxiliary datasets such as JAFFE and CK. To predict emotions, we used a second approach based on geometric features and facial landmarks to calculate and transmit the feature vector to the SVM model. The results show that the ResNet50 model outperforms all other emotion prediction models in real time by maximizing.

“…Moreover, limited studies have been done in the area of mobile travel apps [4,10]. Past scholars such as Izzah et al [11] researched an android-based city tour app, Ali et al [12] studied mobile apps for digitisation facilities management, and Ayyal Awwad [13] examined mobile location services. Indeed, Scopus database evidence that De Reuver et al [14], Fang et al [15], Ho et al [16], Tian et al [17], and Wu and Lee [18], were the leading scholars on mobile travel apps.…”

Section: Introductionmentioning

confidence: 99%

Mobile Travel Apps Engagement: Measuring Tourists’ Perception

Imtiaz

Suki

2022

Mobile travel apps have revolutionised the way people book flights, hotels, and excursions, as well as how travel firms contact clients. This study examines tourists’ perception of mobile travel apps engagement in Pakistan. A field intercept survey was distributed to 500 respondents approached at tourism sites with a high flow of tourists in Northern Pakistan. Data were analyzed using exploratory factor analysis (EFA) via the Statistical Package for Social Sciences (SPSS) version 23 and conﬁrmatory factor analysis (CFA) the Partial Least Square-Structural Equation Modeling (PLS-SEM) approach using Smart-PLS 3.0 software. The EFA analysis by using the principal component method of extraction and the varimax method of rotation yielded five factors. Respondents heavily emphasize item TS2 “I am very satisfied with the mobile travel apps” of tourist satisfaction factor. Likewise, this item emerged as the most dominant in the CFA analysis, followed by item TS3 "I am happy with the mobile travel apps", and item TS4 "I am satisfied with the way mobile travel app organizes the trip". The findings are vital to advancing extant mobile travel apps engagement research, while also guiding their developers to better design mobile travel apps to boost tourists’ trust and satisfaction. Direction for further research is furnished.