scholarly journals Personalized Emotion Recognition and Emotion Prediction System Based on Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenqiang Tian
Keyword(s):  
Cloud Computing ◽  
Emotion Recognition ◽  
Face Detection ◽  
Facial Emotion ◽  
Emotional States ◽  
Prediction System ◽  
Computing Technology ◽  
Recognition Method ◽  
Traditional Methods ◽  
Cloud Data

Promoting economic development and improving people’s quality of life have a lot to do with the continuous improvement of cloud computing technology and the rapid expansion of applications. Emotions play an important role in all aspects of human life. It is difficult to avoid the influence of inner emotions in people’s behavior and deduction. This article mainly studies the personalized emotion recognition and emotion prediction system based on cloud computing. This paper proposes a method of intelligently identifying users’ emotional states through the use of cloud computing. First, an emotional induction experiment is designed to induce the testers’ positive, neutral, and negative three basic emotional states and collect cloud data and EEG under different emotional states. Then, the cloud data is processed and analyzed to extract emotional features. After that, this paper constructs a facial emotion prediction system based on cloud computing data model, which consists of face detection and facial emotion recognition. The system uses the SVM algorithm for face detection, uses the temporal feature algorithm for facial emotion analysis, and finally uses the classification method of machine learning to classify emotions, so as to realize the purpose of identifying the user’s emotional state through cloud computing technology. Experimental data shows that the EEG signal emotion recognition method based on time domain features performs best has better generalization ability and is improved by 6.3% on the basis of traditional methods. The experimental results show that the personalized emotion recognition method based on cloud computing is more effective than traditional methods.

scholarly journals Identification of Various Security Issues, Threats and A Novel Service Model in Cloud Computing

2020 ◽  
Vol 10 (1) ◽  
pp. 321-325
Keyword(s):  
Cloud Computing ◽  
Cloud Storage ◽  
Sudden Change ◽  
Third Party ◽  
Security And Privacy ◽  
Security Measures ◽  
Computing Technology ◽  
Service Model ◽  
Cloud Data ◽  
Security Issues

Cloud Computing is a robust, less cost, and an effective platform for providing services. Nowadays, it is applied in various services such as consumer business or Information Technology (IT) carried over the Internet. This cloud computing has some risks of security because, the services which are required for its effective compilation is outsources often by the third party providers. This makes the cloud computing more hard to maintain and monitor the security and privacy of data and also its support. This sudden change in the process of storing data towards the cloud computing technology improved the concerns about different issues in security and also the various threats present in this cloud storage. In the concept of security in cloud storage, various threats and challenges are noted by recent researchers. Hence, an effective framework of providing security is required. The main aim of this paper is to analyze various issues in securing the cloud data threats present in the cloud storage and to propose a novel methodology to secure it. This paper also identifies the most crucial components that can be incorporated in the already existing security measures while designing the storage systems based on cloud. This study also provides us to identify all the available solutions for the challenges of security and privacy in cloud storage.

scholarly journals Two-Stage Recognition and beyond for Compound Facial Emotion Recognition

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2847
Author(s):  
Dorota Kamińska ◽  
Kadir Aktas ◽  
Davit Rizhinashvili ◽  
Danila Kuklyanov ◽  
Abdallah Hussein Sham ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Complex Problem ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Facial Features ◽  
Recognition Method ◽  
Two Stage ◽  
Facial Images ◽  
Emotion Labels

Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.

scholarly journals Secure Auditing and Intelligent Compression in Cloud

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1164-1169
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Computing Technology ◽  
Cloud Data ◽  
Cloud Resource ◽  
Single Instance

As the cloud computing technology develops throughout the decennary, externalising data to store using cloud resource becomes a trend, which is benefited in heavy data management and maintenance. Notwithstanding, since the externalized cloud depository is not fully reliable, while achieving integrity auditing it elevate security treat on how to realize single instance storage in cloud. In this work, we study the problem of integrity auditing and secure intelligent compression on cloud data. Peculiarly, directing at attaining both eliminating duplicate copies of repeating data i.e., secure deduplication and integrity of data in cloud, we propose an auditing entity with a perpetuation of a MapReduce cloud, which helps audit the integrity as well as uploading of data after clients generate data tags having been collected in cloud.

Autonomous learning of foreign language based on facial emotion recognition and cloud computing

2020 ◽  
pp. 1-12
Author(s):  
Yan Gong ◽  
Sha Rina
Keyword(s):  
Cloud Computing ◽  
Foreign Language ◽  
Emotion Recognition ◽  
Recognition Algorithm ◽  
Learning System ◽  
Facial Emotion Recognition ◽  
Autonomous Learning ◽  
Foreign Languages ◽  
Facial Emotion ◽  
Operating Efficiency

Due to the limitations of the learning environment and unguided guidance, students’ autonomous learning of foreign languages after class is not effective. In order to improve the efficiency of autonomous learning of foreign languages, this paper builds a foreign language self-learning system based on facial emotion recognition algorithm and cloud computing platform. Moreover, this paper uses emotion recognition algorithms to identify students’ status and guide them to improve students’ autonomous learning and improve the system’s operating efficiency through cloud computing platforms. In addition, this article combines the needs of autonomous learning to perform facial emotion matching and builds the corresponding functional modules of the system according to the requirements of autonomous learning and designs a sophisticated three-level network structure to achieve a balance between detection performance and real-time performance. In order to verify the performance of the system, an experiment was carried out through the accuracy rate of student’s autonomous state emotion recognition and the English improvement of students’ autonomous learning. The research results show that the foreign language autonomous learning system constructed in this paper has good performance.

Storage Security and Technical Challenges of Cloud Computing

2013 ◽  
pp. 225-240
Author(s):  
Shantanu Pal
Keyword(s):  
Cloud Computing ◽  
Cloud Service ◽  
Clear Understanding ◽  
Security Measures ◽  
Computing Environment ◽  
Computing Technology ◽  
Cloud Computing Environment ◽  
Storage Security ◽  
Cloud Data ◽  
The Many

Cloud computing has leaped ahead as one of the biggest technological advances of the present time. In cloud, users can upload or retrieve their desired data from anywhere in the world at anytime, making this the most important and primary function in cloud computing technology. While this technology reduces the geographical barriers and improves the scalability in the way we compute, keeping data in a Cloud Data Center (CDC) faces numerous challenges from unauthorized users and hackers within the system. Creating proper Service Level Agreements (SLA) and providing high-end storage security is the biggest barrier being developed for better Quality of Service (QoS) and implementation of a safer cloud computing environment for the Cloud Service Users (CSU) as well as for the Cloud Service Providers (CSP). Therefore, cloud applications need to have increased QoS and effective security measures and policies set in place to provide better services and to decline unauthorized access. The purpose of this chapter is to examine the cloud computing technology behind innovative business approaches and establishing SLA in cloud computing applications. This chapter provides a clear understanding of different cloud computing security challenges, risks, attacks, and solutions that exist in the present heterogeneous cloud computing environment. Storage security, different cloud infrastructures, the many advantages, and limitations are also discussed.

Enhanced Load Balancing Approach for Cloud Data Center Load Optimization

2021 ◽  
Vol 18 (4) ◽  
pp. 1270-1274
Author(s):  
J. Prassanna ◽  
V. Neelanarayanan
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Data Center ◽  
Resource Control ◽  
Resource Optimization ◽  
Computing Technology ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Load Optimization ◽  
Related Data

Cloud computing is a most popular technology that has huge response in markets. Cloud computing has the potential to access applications and their related data via the Internet anywhere. Most companies already pay for the use of cloud resources for storage purposes and ultimately reduce the costs of infrastructure spending. They can make use of this technology for accessing to company applications like pay-as-you-go approach. One of the major obstacles associated with cloud computing technology is to better optimization of resource allocation. Assigning of workloads to the servers using load balancing techniques is used to achieve less response time and better resource optimization across the server. Resource control and balance of load are the major conflicts in the cloud environment, which is why there are different load balancing algorithms, each with its own advantages and disadvantage. In order to achieve a better economy and mutual benefit, efficient algorithms can be derived simultaneously by optimizing servers, green computing and better utilization of resources. The objective of this paper is to analyze and enhance existing load balancing algorithms.

scholarly journals Effective Facial Emotion Recognition using Convolutional Neural Network Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 4351-4354
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Face Detection ◽  
Vital Role ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Contact Frequency ◽  
Static Images ◽  
Neural Network Algorithm

This paper presents the idea related to automated live facial emotion recognition through image processing and artificial intelligence (AI) techniques. It is a challenging task for a computer vision to recognize as same as humans through AI. Face detection plays a vital role in emotion recognition. Emotions are classified as happy, sad, disgust, angry, neutral, fear, and surprise. Other aspects such as speech, eye contact, frequency of the voice, and heartbeat are considered. Nowadays face recognition is more efficient and used for many real-time applications due to security purposes. We detect emotion by scanning (static) images or with the (dynamic) recording. Features extracting can be done like eyes, nose, and mouth for face detection. The convolutional neural network (CNN) algorithm follows steps as max-pooling (maximum feature extraction) and flattening.

scholarly journals Continuous Facial Emotion Recognition Method Based on Deep Learning of Academic Emotions

2020 ◽  
Vol 32 (10) ◽  
pp. 3243
Author(s):  
Szu-Yin Lin ◽  
Chao-Ming Wu ◽  
Shih-Lun Chen ◽  
Ting-Lan Lin ◽  
Yi-Wen Tseng
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Recognition Method ◽  
Academic Emotions
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