Classification Of Media Research Articles

Evaluation of CNN Models with Transfer Learning in Art Media Classification in Terms of Accuracy and Class Relationship

Evaluation of CNN Models with Transfer Learning in Art Media Classification in Terms of Accuracy and Class Relationship

Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network

With the development of technology, mobile robots are increasingly deployed in real-world environments. To enable robots to work safely in a variety of terrain environments, we proposed a ground-type detection method based on the Hilbert–Huang transform (HHT) and attention-based spatiotemporal coupled network. Taking a dataset containing multiple sets of robot signals from a Kaggle competition as an example; we use the proposed method to classify the signals and thus achieve a terrain classification of the robot’s location. Firstly, the signal data were processed using the discrete wavelet transform for noise reduction, and all channels in the dataset were ranked by importance using the permutation importance method. Next, the instantaneous frequencies of the two most important channels were extracted using the HHT and added to the original dataset to expand the feature dimension. Then the features in the expanded dataset were extracted by the convolutional neural network, long short-term memory, and attention module. Afterward, the fully extracted features were passed into the fully connected layer for classification, and an average classification accuracy of 83.14% was obtained. The effectiveness of each part in our method was demonstrated using ablation experiments. Finally, we compared our method with some common methods in the field and found that our method obtained the highest classification accuracy, proving the superiority of the proposed method.

A customized deep learning-based framework for classification and analysis of social media posts to enhance the Hajj and Umrah services

Each year, millions of pilgrims come to Saudi Arabia to perform Hajj and Umrah. To help pilgrims with their difficulties and make plans to enhance the Hajj and Umrah services, this article offers a deep learning-based framework to categorize and analyze pilgrims’ posts on social media networks X (formerly Twitter), Facebook and Instagram. We extracted Arabic posts related to Hajj/Umrah then tokenized and pre-processed the dataset to remove unnecessary parts such as punctuation. Posts are manually labeled into five classes: Health, Organization, Security, Services, and Worship. Labeled posts are collected to build a Long Short-Term Memory (LSTM) network classifier for deep learning. To increase the prediction accuracy, we customized the LSTM classification layer and used the sum of squares error (SSE) loss function instead of the default cross-entropy loss function. A rigorous simulation is run to assess the system’s effectiveness. Every time a simulation round occurs, the trained network is tasked with identifying the classes of unlabeled posts (testing data). Similar tests are performed using the K-Nearest Neighbors (KNN) and Linear Discriminant Analysis (LDA) algorithms for comparative analysis. Accuracy, confusion matrix, precision, sensitivity, specificity, and F1 score are used to quantify the system performance. The maximum mean accuracy of the proposed framework, KNN and LDA are 85.65%, 68.47%, and 59.06% respectively. The proposed framework needs to be integrated into a larger system called the Intelligent Pilgrim Service System (IPSS). We described the IPSS architecture, which comprises modules for data generation, data storage (on the cloud), and a control center. We described a hashtag mechanism as a part of the IPSS. This hashtag mechanism will provide both general and focused advice to millions of pilgrims related to their social media posts. We discussed scenarios of how the IPSS can enhance the Hajj and Umrah services.

Spatial-Temporal Aware Disaster Topic Recognition in Social Media Text Using SageMaker

With the popularization and widespread use of social media, a large amount of information is shared and disseminated by users on the platforms, including disaster-related text messages. The disaster information contained in these social media text messages is of great value for emergency response, disaster research, and the understanding of public opinion. In the task of text classification of social media disaster topics, previous approaches generally only semantically analyze the text itself to determine its relevance to the disaster, and this paper considers a new perspective that combines the spatio-temporal attributes of social media texts and textual information to complete the text classification task. Based on the above idea, this paper constructs a heterogeneous graph on the dataset using the temporal and spatial attributes of blog posts, initializes the blog nodes using BERT features, and learns the blog features through relational graph convolution operation. Experiments on the SageMaker platform use BERT as a baseline model, while blog post features are initialized using this model. The experimental results show that the spatio-temporal model achieves better performance compared to the baseline model.

DEVELOPMENT OF COMPUTER BASED PAI LEARNING MEDIA, MICROSOFT POWERT POINT AND INTERNET MEDIA

The urgency of learning media is to overcome the limitations of experience, to concretize abstract concepts, to overcome the limitations of space and time, to have direct interaction with the environment, to produce uniformity of observations, to instill true, concrete and realistic basic concepts, to arouse motivation and stimulate children to learn, to arouse the desires and interests of the teacher and provide an integral experience Classification of learning media, namely visual media, audio, audio visual, print, models, reality, computers in the learning process and multimedia. Computer-based learning media is the use of computers as a medium for conveying learning information, practice questions, feedback, and scores of students' answers. Computer-based learning media consists of: PowerPoint presentations, CD/DVD/ Interactive Learning Multimedia, learning videos and the internet. Computer-based learning supports curriculum implementation, helps efforts to increase interest in learning, and becomes a complement to learning resources. The presence of computer technology in learning only acts as a complement, addition (supplement) or tool for teachers.

INFORMATION SPACE

The purpose. To investigate the concept of information space, to identify its types, to analyze its components, to study the mechanisms of formation of information spaces in modern conditions. Methods. The concept of both information and space, as well as their combination, is studied. The most common uses of the term "information space" in Ukrainian-language and English-language sources are identified. The mutual circulation of information both in real life and in the telecommunications space is studied. The mechanisms of formation of the individual's information space are studied. The concepts of "information space of the state" and "national information space" are considered, and their comparison is given. Results. The composition of the information space is clarified, objects – sources of information and transmission channels-are studied. For information sources, their hierarchy is given. Attention is paid to mass media and the subject of their functioning. The classification of mass media, their functions and features as social institutions are studied. The factors that influence the formation of an individual's information space are highlighted. The definition of the individual's information space is given. Examples of integrated information spaces and examples of disintegration of information spaces are given. Scientific novelty. The factors that influence the formation of an individual's information space are highlighted. The definition of mass media in a broad sense has been clarified. It is established that the basis of an individual's information space on the Internet is his personal social network, namely, the web PSN. A key factor in the formation of the individual's information space in our time is highlighted. The factors that encourage an individual to adjust their own information space are studied. Classification of subspaces of the individual's information space is given. Practical significance. The concept of information space is multi-valued and multifaceted. Identifying the components of the information space and its parameters, the relative location of existing information spaces and their place in the global information space will contribute to solving many practical tasks – from business tasks to conducting political information campaigns (influence on the electorate before elections - for example). For example, this will help improve targeting, and therefore create better targeted advertising. For the average user, the described mechanism for creating a personal information space, factors that affect the formation of the information space and its correction, is important. Knowing the above, the individual will create a more optimal and high-quality personal social network, as well as an information space based on it.

Clicks and Conversions: Unveiling the Power of Machine Learning in Social Media Ad Classification

The classification of social media ads plays a crucial role in determining the likelihood of target audience engagement and product purchases. This application of data science in marketing offers significant potential for improving ad targeting strategies. This article aims to provide insights into the analysis of social media ads for effective audience classification. By understanding the factors that influence target audience behavior, marketers can optimize their ad campaigns and enhance customer conversion rates. Through the utilization of data science techniques, this study offers valuable guidance on how to analyze social media ads and classify target audiences more accurately

PENGEMBANGAN MEDIA PEMBELAJARAN ULAR TANGGA PADA MATA PELAJARAN IPS TERPADU

Learning media can help teachers by offering explanations for the lessons that need to be taught. The purpose of this project is to create instructional materials for Snakes and Ladders in social studies for class VIII at SMP Panca Setya 2 Sintang in the academic year 2022–2023. Research and development research utilizing the ADDIE model is the research methodology used. Studying through experiments design for a single subject experiment. Both qualitative and quantitative data are present in the research data. For the school year 2022–2023, 25 eighth-grade SMP Panca Setya 2 Sintang students served as the research subjects. using surveys, observation, and documentation as data collecting methods. Product practicality and feasibility assessments are part of data analysis. The findings of this study are presented in the form of educational media materials for the social studies game of snakes and ladders for class VIII junior high school pupils. Based on the media's classification as very good, the feasibility evaluation was made. The material's viability is rated as being very good. Evaluation of usefulness using very doable standards. According to the questionnaire, the SMP Panca Setya 2 Sintang IPS students' and teachers' responses in terms of materials and media are rated as meeting very good requirements

Using the AraBERT Model for Customer Satisfaction Classification of Telecom Sectors in Saudi Arabia.

Customer satisfaction and loyalty are essential for every business. Feedback prediction and social media classification are crucial and play a key role in accurately identifying customer satisfaction. This paper presents sentiment analysis-based customer feedback prediction based on Twitter Arabic datasets of telecommunications companies in Saudi Arabia. The human brain, which contains billions of neurons, provides feedback based on the current and past experience provided by the services and other related stakeholders. Artificial Intelligent (AI) based methods, parallel to human brain processing methods such as Deep Learning (DL) algorithms, are famous for classifying and analyzing such datasets. Comparing the Arabic Dataset to English, it is pretty challenging for typical methods to outperform in the classification or prediction tasks. Therefore, the Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model was used and analyzed with various parameters such as activation functions and topologies and simulated customer satisfaction prediction takes using Arabic Twitter datasets. The prediction results were compared with two famous DL algorithms: Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Results show that these methods have been successfully applied and obtained highly accurate classification results. AraBERT achieved the best prediction accuracy among the three ML methods, especially with Mobily and STC datasets.

Implementation of Bot Telegram as Broadcasting Media Classification Results of Convolutional Neural Network (CNN) Images of Rice Plant Leaves

Rice plants play an important role in the life of the Indonesian people because rice is the raw material for rice as a staple food. The rice production process does not rule out the possibility of interference by pests and diseases resulting in losses that cause crop failure. Meanwhile, pests on rice plants can be caused by various types, namely types of fungi (leafblast, hispa, brownspot) and types of nuisance animals. In this research, it will be carried out how to classify the image of rice plant leaves using the deep learning Convolutional Neural Network (CNN) algorithm, then the results of the classification are sent to users by utilizing the telegram chat application. The rice plant leaf image dataset is grouped into 4 groups (leafblast, brownspot, hispa and healthy). From several experiments it can be seen the results of system performance, namely the classification speed takes 30-60 seconds.

Application of a multi-channel attention mechanism in text classification of new media

Application of a multi-channel attention mechanism in text classification of new media

Application of a multi-channel attention mechanism in text classification of new media

Application of a multi-channel attention mechanism in text classification of new media

Research on multi-label user classification of social media based on ML-KNN algorithm

Several research studies have been conducted on multi-label classification algorithms for text and images, but few have been conducted on multi-label classification for users. Moreover, the existing multi-label user classification algorithm does not provide an effective representation of users, and it is difficult to use directly in social media scenarios. By analyzing complex social networks, this paper aims to achieve multi-label classification of users based on research in single-label classification.Considering the limitations of existing research, this paper proposes a user topic classification method based on heterogeneous networks as well as a user multi-label classification method based on community detection. The model is trained using the ML-KNN multi-label classification algorithm. In actual scenarios, the algorithm is more effective than existing multi-label classification methods when applied to multi-label classification tasks for social media users.According to the results of the analysis, the algorithm has a high level of accuracy in classifying different theme users into a variety of different scenarios using different theme users. Furthermore, this study contributes to the advancement of classification research by expanding its perspective.

The Influence of Social Media for Millennials in the Digital Age

In the era of technology 4.0 which is growing, almost all fields have used the internet and have become a necessity in everyday life, both among children, millennials, adults, the elderly, the elderly and so on. Including social media which has a very big influence in everyone's life. Someone who initially does not understand technology with the existence of social media becomes aware, and social media can also change a person's attitude and nature. In the community, especially millennials, social media has become a necessity that cannot be separated from everyday life and has become a lifestyle where millennials do not spend a day without opening social media. If it is explored further in depth during its development period, millennials need direction and guidance both at school, at home and in the surrounding environment because millennials are still looking for their identity by hanging out with friends their age. The purpose of this study is to determine the positive and negative impacts of social media, to know the characteristics of social media, to know the types of social media, and to understand what the millennial generation is like. The results of this study increase creativity, means of communication, facilitate communication, a place for achievement, a place for learning media. While the negative impacts of social media such as misuse of technology, decreased focus on learning, high crime rates, cyberbullying. The specific characteristics of Social Media include: Reach, Accessibility, Use, Actuality, Persistence. Classification of Social Media include: Collaborative projects (Wikipedia), Blogs and microblogs (twitter), Content (youtube), Social networking sites (Facebook), Virtual game world (online games), Victure social world (second life). Millennial Generation itself When viewed from the age group, the millennial generation is a generation that is currently aged between 15-34 years..

HOW DO ENGLISH TEACHERS PERCEIVE ON INNOVATIVE MEDIA? DESCRIPTIVE ANALYSIS AT SINTANG'S SENIOR HIGH SCHOOLS

Innovative learning media is essential in supporting the classroom's English learning process. Its use can increase the students' motivation, which affects the absorption of the material being taught. However, many teachers idolized using only monotonous media for teaching English. Thus, by following this phenomenon, the researchers aim to analyze the use of innovative media in Sintang High School, West Kalimantan. This research is important to know the phenomena that can be the future suggestion to the stakeholder in using media in Sintang because there is no similar research conducted. The method that the researcher used was a qualitative method. Some English teachers from several Senior High Schools were selected based on the purposive sampling method, which is public schools in the district of Sintang. Researchers interviewed the teachers and the students to get the validity. From this interview, the researchers know that the innovative media is needed in English language teaching. It is supported by their teaching experience in using innovative media in the classification of ICT and non-ICT media. however, the use of innovative media in teaching has several obstacles, such as internet connection, teacher ability in using technology and teacher creativity to prepare the media.

Warm media: reflections on the classification of media in the digital age

Nesse artigo pretende-se refletir sobre a teoria dos meios de Marshall McLuhan, e analisar sua classificação dos meios de comunicação como sendo frios e quentes. Parte-se do pressuposto de que desde o desenvolvimento dos meios digitais também floresceu uma maneira diferente de se relacionar através dos meios, o que acabou promovendo novos comportamentos sociais e novas percepções quanto à informação no usuário dos meios digitais. Por isso, pretende-se refletir sobre a possibilidade de os meios digitais não se encaixarem nessa classificação, sendo necessária uma revisão desse conceito, e sugerindo um termo intermediário: meios mornos. ////// In this article we intend to reflect on Marshall McLuhan's theory of media and analyze his classification of the media as being cold and hot. It is assumed that since the development of digital media, a different way of relating through the media has also flourished, which ended up promoting new social behaviors and new perceptions of information in the user of digital media. Therefore, it is intended to reflect on the possibility that digital media do not fit this classification, requiring a review of this concept, and suggesting an intermediate term: warm media.

Classification of social media users with generalized functional data analysis

Technological advancement has made possible the collection of data from social media platforms at unprecedented speed and volume. Current methods for analyzing such data lack interpretability, are computationally intensive, or require a rigid data specification. Functional data analysis enables the development of a flexible, yet interpretable, modeling framework to extract lower-dimensional relevant features of a user's posting behavior on social media, based on their posting activity over time. The extracted features can then be used to discriminate a malicious user from a genuine one. The proposed methodology can classify a binary time series in a computationally efficient manner and provides more insights into the posting behavior of social media agents. Performance of the method is illustrated numerically in simulation studies and on a motivating Twitter data set. The developed methods are applicable to other social media data, such as Facebook, Instagram, Reddit, or TikTok, or any form of digital interaction where the user's posting behavior is indicative of their user class.

Prognostic factors of hearing outcome in patients with chronic suppurative otitis media following tympanoplasty: a retrospective cohort study.

Research on the factors affecting the functional outcome of tympanoplasty in chronic suppurative otitis media (CSOM) is limited. This study aimed to investigate the prognostic factors of hearing outcome in CSOM patients following tympanoplasty. The multivariate logistic regression analysis is used to evaluate the influence of demographic, audiological, clinical and disease factors on the postoperative prognosis of CSOM. A total of 179 CSOM patients who received surgery between January 2016 and December 2019 were retrospectively included. The inclusion criteria are mainly based on clinical classification of otitis media and surgical classification guidelines [2012]. These patients were divided into an effective group (n=132) and a non-effective group (n=47) according to their postoperative air-bone gap (ABG) value. Multivariate analysis was performed according to the pure tone hearing results 1 week before operation and 4 weeks after operation. The type of preoperative hearing impairment, middle ear cholesteatoma, ossicular chain status, tympanic perforation site, repair material, tympanum sclerosis, and surgical methods were significantly different between the effective and non-effective groups (all P<0.01). The speech reception threshold (SRT) (36.88±15.54 vs. 27.68±13.75, P<0.001) and ABG (18.53±9.30 vs. 10.89±5.07, P<0.001) values were markedly reduced after surgery in the effective group but not in the non-effective group. Furthermore, the postoperative decreases in the SRT (9.20±9.69 vs. 2.021±7.34, P<0.001) and ABG (7.64±8.57 vs. 0.45±7.322, P<0.001) values were considerably higher in the effective group than the non-effective group. After multivariate logistic regression analysis, we found that the type of preoperative hearing impairment [P=0.031; odds ratio (OR) =2.378], ossicular chain status (P=0.002; OR =4.430). Our research shows that the curative effect of conductive deafness before operation is 2.378 times better than that of mixed deafness, the curative effect of complete and unfixed ossicular chain before operation is 1.080 times better than that of interrupted ossicular chain, and the curative effect of complete ossicular chain but fixed ossicular chain is 4.430 times better than that of interrupted ossicular chain. Our findings may help to predict the postoperative prognosis of patients, thus facilitating the development of corresponding treatment strategies.

Tourism work, media & COVID-19: A changed narrative?

Major crises, such as COVID-19, have had a dramatic impact on tourism work. This paper investigates changes in news media reporting of tourism work from pre-pandemic norms, employing McQuail's classification for media and mass communications. We interrogate 664 news articles across two comparative periods. Findings indicate that news media themes in 2019 were often found in the ‘soft news’ sections, whereas the 2020 news elevated into ‘hard news’ sections, highlighting job losses, precarious work and community spirit. The analysis points to a more sympathetic narrative in the media about tourism employment. Theoretically, the paper is original in applying McQuail's media classification to tourism employment in a crisis context, contributing to our understanding of potential impacts of news media reporting.

Crossmodal bipolar attention for multimodal classification on social media

Multimodal classification of social media is used to classify data from different modalities into different categories, which is essential for understanding user behavior on the web. In this paper, we focus on classifying image-text pairs, specifically user-generated content on social media. Recently, the transformer network, a kind of self-attention network, has been widely studied in the disciplines of visual computing and language processing. In the attention mechanism, positive correlation is considered. However, multimedia content posted on social media is diverse. Images and text are not always consistent, and contrary information is also helpful for representation. Therefore, it is equally important to detect conflicts based on negative or inverse attention. Inspired by the attention mechanism, we propose a novel model, namely Crossmodal Bipolar Attention Network (CBAN). Different from existing positive dot-product and additive attention mechanisms, we propose a bipolar attention mechanism, which fuses visual and textual information through their direct and inverse semantic relationships to classify multimodal data. We conducted experiments on multiple multimodal classification data sets, for performing sentiment analysis, sarcasm detection, crisis categorization and hate-speech detection. Experimental results show that our proposed CBAN consistently outperforms state-of-the-art methods in all classification tasks.