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Evaluation of Hotel Performance with Sentiment Analysis by Deep Learning Techniques
Get PDFThe subject of sentiment analysis through social media sites has witnessed significant development due to the increasing reliance of people on social media in advertising and marketing, especially after the Corona pandemic. There is no doubt that the prevalence of the Arabic language makes it considered one of the most important languages all over the world. Through human comments, it can know things if they are positive or negative. But in fact, the comments are many, and it takes work to evaluate the place or the product through a detailed reading of each comment. Therefore, this study applied deep learning approaches to this issue to provide final results that could be utilized to differentiate between the comments in the dataset. Arabic Sentiment Analysis was used and gave a percentage for each positive and negative commentary. This work used eight methods of deep learning techniques after using Fast Text as embedding, except Ara BERT. These techniques are the transformer (AraBERT), RNN (Long short-term memory (LSTM), Bidirectional long-short term memory (BI-LSTM), Gated recurrent units (GRUs), Bidirectional Gated recurrent units (BI-GRU)), CNN (like ALEXNET, proposed CNN), and ensemble model (CNN with BI-GRU). The Hotel Arabic Reviews Dataset was utilized to test the models. This paper obtained the following results. In the Ara BERT model, the accuracy is 96.442%. In CNN, like the Alex Net model, the accuracy is 93.78%. In the suggested CNN model, the accuracy is 94.43%. In the suggested LSTM model, the accuracy is 95%. In the suggested BI-LSTM model, the accuracy is 95.11%. The accuracy of the suggested GRU model is 95.07%. The accuracy of the suggested BI-GRU model is 95.02%. The accuracy is 94.52% in the Ensemble CNN with BI-GRU model that has been proposed. Consequently, the AraBERT outperformed the other approaches in terms of accuracy. Because the AraBERT has already been trained on some Arabic Wikipedia entries. The LSTM, BI-LSTM, GRU, and BI-GRU, on the other hand, had comparable outcomes
A Novel Routing Protocol-Based Data Transmission to Enhance the Quality of Service for Internet of Medical Things Using 5G
Get PDFWhen it comes to improving people's health, various forms of smart city applications are put to use in the area of smart healthcare. Health care providers are increasingly using the internet of medical things (IoMT) as an environment of linked healthcare systems, computational capabilities, and health monitors to improve the quality of service (QoS). Smart healthcare devices transmit data to IoMT and it requires an efficient solution. IoMT's and 5G networks' importance necessitates the development and testing of a novel multi-hop reinforced clustering-based routing protocol (MRCRP) to improve health data transmission's QoS. Cluster head selection is carried out using an energy-efficient approach to enhance resource usage. Min-max normalization is used to transform original data into meaningful data for more analysis. Using principal component analysis (PCA), relevant features can be extracted. The proposed routing technique is then employed to effectively transmit the data. The suggested approach's performance measures, such as delay, throughput, energy consumption, and packet drop ratio are studied, as is a comparative analysis with standard routing protocols.
Word Embedding for High Performance Cross-Language Plagiarism Detection Techniques
Get PDFAcademic plagiarism has become a serious concern as it leads to the retardation of scientific progress and violation of intellectual property. In this context, we make a study aiming at the detection of cross-linguistic plagiarism based on Natural language Preprocessing (NLP), Embedding Techniques, and Deep Learning. Many systems have been developed to tackle this problem, and many rely on machine learning and deep learning methods. In this paper, we propose Cross-language Plagiarism Detection (CL-PD) method based on Doc2Vec embedding techniques and a Siamese Long Short-Term Memory (SLSTM) model. Embedding techniques help capture the text's contextual meaning and improve the CL-PD system's performance. To show the effectiveness of our method, we conducted a comparative study with other techniques such as GloVe, FastText, BERT, and Sen2Vec on a dataset combining PAN11, JRC-Acquis, Europarl, and Wikipedia. The experiments for the Spanish-English language pair show that Doc2Vec+SLSTM achieve the best results compared to other relevant models, with an accuracy of 99.81%, a precision of 99.75%, a recall of 99.88%, an f-score of 99.70%, and a very small loss in the test phase
A Conceptual Approach to an AI-Based Adaptive Study Support System for Individualized Higher Education
Get PDFIn the context of the digital transformation, the targeted implementation of AI-based or AI-supported technologies in "teaching & learning" as well as "administration & service" holds considerable potential for organizational change and quality enhancement for higher education institutions. The use of AI in higher education teaching and services lags behind the level in research. Therefore, holistic solutions must be planned and implemented in unity of teaching and research for the AI-based support of the stakeholders’ inclusive administration, the further development or the establishment of new digital study programs and offers as well as the prospective qualification of university staff in the field of AI. The solutions must be analyzed, systematized, and structured to generate a conceptual approach via an integrated architecture with adaptive services.
This paper is an evolution of the concept presented at the Learning Ideas Conference 2022 and includes, in addition to explanations of the current state of the arts, the presentation of a systems-oriented approach for AI in higher education as well as the conception of the student lifecycle management at the author’s university. The paper ends with the presentation of a concept regarding a decision support system for individualized studying
The GW Mobile Learning Center: Mixed-Reality within an Immersive and Interactive Learning Setting
Get PDFThe Community Medi-Corps Program - designed and implemented by the George Washington University (GW) School of Medicine and Health Sciences (SMHS) faculty with Growth and Opportunity Virginia funding (GO Virginia) - is aimed at leveraging the power of community, educational institutions, mentors, industry, and business partners to close the opportunity gap, transform student learning, and enrich the regional workforce. This program transforms educational experience through innovative virtual reality, augmented reality, and a mix between the two that is the enhanced reality (e-REAL). Students will be better prepared in the pathways they choose for high demand health and life sciences industry jobs that will help grow the economy
A Design for Gamified On-the-job Training
Get PDFThe main purpose of this article is to review the factors that impact employee motivation in on-the-job training and the extent to which game mechanics can be used to increase motivation and expected learning outcomes. The choice for this topic is based on the lack of academic literature on gamification in the work environment – more so, relating to on-the-job training. Contrarily, the necessity for the improvement of user’s motivation in participating in such training programmes is already well established. This study presents the JT Framework for creating gamified on-the-job training. The baseline theories applied for the framework include: the flow theory, FODEM and Octalysis framework, which were combined with various motivational theories, game mechanics and studies on user types. The framework operates in the intersection within business ROI, user experience design and behavioural science, which need to be considered when evaluating the framework. In this research, a series of interviews were conducted to assess the framework model from the user’s perspective
New Technologies for Inclusive Learning for Students with Special Educational Needs
Get PDFIn recent years, in the context of the educational reform, dominant position is held by the social demand for the inclusion of all students in the regular classroom and the request to redesign the educational process to align with the individualized needs of students. Τhe concept of inclusion, which view the heterogeneity in the light of social justice and equality, refers not simply to the placement of the SEN students in the mainstream school, but basically, to their dynamic engagement in every aspect of the educational process and in the social interactions that flow from it. “E-inclusive” pedagogy refers to teachers’ decisions to providing their students innovative ways of learning and alternative means of completing their tasks, by incorporating the technology in the educational activities. The aim of this paper is to propose tech tools and e-services for the accessibility and active participation of students with special educational needs in teaching and learning procedures of the ordinary classroom and examine the role of teachers in realizing their inclusion / e-inclusion, as the main facilitators and modulators of the classroom settings to an open learning and development ecosystem. The results showed that teachers who provide authentic opportunities for interaction and learning for all their students and incorporate, flexibly, new technologies into their teaching strategies to meet their unique needs, contribute significantly to their acquisition of academic but mainly functional life skills, preparing them for substantial employment and integration opportunities in community life
Determining the Optimal Number of Clusters using Silhouette Score as a Data Mining Technique
Get PDFThe identification of the same objects is very important in determining the similarity between different objects. Nowadays, there are several techniques that allow us to divide objects into different groups that differ from one to another. In order to have the best separation between the clusters, it is required that the optimal determination of the number of clusters of a corpus be made in advance. In our research, the Silhouette score technique was used in order to make the optimal determination of this number of clusters. The application of such a technique was done through the Python language, and a corpus of unstructured job vacancy data was used. After determining the optimal number, at the end we present these clusters and the similarity between them, this presentation will be done in the form of a graph in a suitable format
Human Action Behavior Recognition in Still Images with Proposed Frames Selection Using Transfer Learning
Get PDFOne of the most difficult challenges is recognizing human actions., especially in still images where there isn't much movement. Therefore, Using the transfer learning strategy, we suggested a technique for identifying human action., which consists of training some of the layers of deep learning techniques while freezing others. Also presented a way for data split, which is to choose some frames because we are working on a large dataset such as ucf-101, and this method is summarized by discovering the features for each frame, then clustering the elements, and then choosing a percentage of each cluster for training and test data. We used three techniques. They are vgg16, inceptionv3, and xception. The proposed models have been implemented on UCF-101 Dataset. Depending on three data split methods with the dataset, the random split method, and the proposed split method, the inceptionv3 achieved the highest accuracy. In contrast, the vgg16 achieved the least accuracy, and the accuracy of the xception was close to that of the inceptionv3. By comparing the size of the dataset, the proposed methods achieved good results: the vgg16 in the proposed split attained an accuracy of 92.5%, the inception v3 in the proposed split attained an accuracy of 98.12%, and the xception in the proposed split attained an accuracy of 95.16%. The VGG16 network is simple, so the VGG16 is less accurate. While the network in inceptionv3, xception, is more extensive and complex, the learning space is more significant, although the network size is more prominent in inceptionv3, xception. We only trained some blocks in the top layer
 
Empirical Studies on the Metaverse-Based Education: A Systematic Review
Get PDF Metaverse is a digital space achieved through a virtual environment that allows people to enjoy immersive experiences and interactions. Metaverse is changing the world in different fields, from education to health. However, using the Metaverse for educational purposes has rarely been discussed. While there are only a few reviews summarizing the findings of studies on the Metaverse, no study has systematically analyzed empirical studies of the Metaverse in education. This study presents a systematic review of empirical studies on Metaverse-based education to fill this gap. The results revealed that Empirical studies were conducted with university students at most, and Additionally, no study focused on middle and primary school students. The results showed that virtual reality is the most common metaverse type used in empirical studies. However, there is a need for empirical work using Mixed reality technology in the educational Metaverse. As a result of examining the student engagement findings in the studies included in the review, it was seen that few experimental studies focused on students' behavioral engagement. It is expected that the findings of this study will guide empirical research on the Metaverse in education