Textual Content Research Articles

Hate Speech Automatic Detection

This study offers a system studying-primarily based absolutely device for automated hate speech detection in textual content the usage of natural Language Processing (NLP) techniques. fashions like Logistic Regression and deep mastering algorithms (LSTM, BERT) are educated on classified datasets with features extracted via TF-IDF. The system evaluates overall overall performance using metrics together with accuracy, precision, keep in mind, and F1-score. A multilingual translation issue powered with the aid of Google Translator enables detection across languages. A Tkinter-based totally GUI allows clients to categorise remarks in actual-time, making sure accessibility and simplicity of use. elegance imbalances are addressed with weighted Logistic Regression, improving detection accuracy. The gadget demonstrates strong overall performance, promoting greater communities with the resource of offering an inexperienced device for computerized hate speech moderation.

Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study.

Studying the impact of COVID-19 on mental health is both compelling and imperative for the health care system's preparedness development. Discovering how pandemic conditions and governmental strategies and measures have impacted mental health is a challenging task. Mental health issues, such as depression and suicidal tendency, are traditionally explored through psychological battery tests and clinical procedures. To address the stigma associated with mental illness, social media is used to examine language patterns in posts related to suicide. This strategy enhances the comprehension and interpretation of suicidal ideation. Despite easy expression via social media, suicidal thoughts remain sensitive and complex to comprehend and detect. Suicidal ideation captures the new suicidal statements used during the COVID-19 pandemic that represents a different context of expressions. In this study, our aim was to detect suicidal ideation by mining textual content extracted from social media by leveraging state-of-the-art natural language processing (NLP) techniques. The work was divided into 2 major phases, one to classify suicidal ideation posts and the other to extract factors that cause suicidal ideation. We proposed a hybrid deep learning-based neural network approach (Bidirectional Encoder Representations from Transformers [BERT]+convolutional neural network [CNN]+long short-term memory [LSTM]) to classify suicidal and nonsuicidal posts. Two state-of-the-art deep learning approaches (CNN and LSTM) were combined based on features (terms) selected from term frequency-inverse document frequency (TF-IDF), Word2vec, and BERT. Explainable artificial intelligence (XAI) was used to extract key factors that contribute to suicidal ideation in order to provide a reliable and sustainable solution. Of 348,110 records, 3154 (0.9%) were selected, resulting in 1338 (42.4%) suicidal and 1816 (57.6%) nonsuicidal instances. The CNN+LSTM+BERT model achieved superior performance, with a precision of 94%, a recall of 95%, an F1-score of 94%, and an accuracy of 93.65%. Considering the dynamic nature of suicidal behavior posts, we proposed a fused architecture that captures both localized and generalized contextual information that is important for understanding the language patterns and predict the evolution of suicidal ideation over time. According to Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) XAI algorithms, there was a drift in the features during and before COVID-19. Due to the COVID-19 pandemic, new features have been added, which leads to suicidal tendencies. In the future, strategies need to be developed to combat this deadly disease.

Spectral Clustering-Based Particle Swarm Optimization Algorithm for Document Clustering

The process of automatically grouping documents into clusters such that the documents in one cluster are very comparable to the documents in the remaining clusters have been known as document clustering. Due to its broad application in a number of fields, including search engines, web mining, and information retrieval, it has been the subject of much research. It involves clustering documents that are identical to one another and calculating how identical they are. It facilitates simple navigation by offering effective document representation as well as visualization. Hence, this research paper plans to perform the document clustering using the nature inspired optimization technique. Initially, the dataset is manually gathered from different sources. Next, the data preparation has been done for extracting the text content from the published documents. These prepared data undergo pre-processing for removing the punctuations, stop words, and lowercase conversion. The features are extracted from these pre-processed data utilizing the Term Frequency-Inverse Document Frequency (TF-IDF) approach for extracting the keywords. The extracted features undergo the final clustering phase employing the spectral clustering algorithm, in which the parameter tuning has been done by the nature inspired optimization algorithm referred as Particle Swarm Optimization (PSO) with the consideration of silhouette score maximization as the objective function. This proposed spectral clustering-PSO clusters the final output into six classes such as data mining, deep learning, image, machine learning, network, and sports respectively. The proposed document clustering model describes its betterment over the remaining techniques with respect to distinct measures. The proposed spectral clustering-PSO in terms of silhouette score is 51.92%, 70.81%, 45.93%, and 20.89% better than JA-GWO, tpLDA, HDMA, and Net2Vec respectively. Similarly, the proposed spectral clustering-PSO in terms of davies bouldin score is 89.69%, 58.48%, 32.67%, and 13.99% advanced than JA-GWO, tpLDA, HDMA, and Net2Vec respectively.

The Role of Artificial Intelligence in Website Creation

This article discusses the role of artificial intelligence (AI) in website creation, highlighting its impact on development processes and user interaction. Modern technologies such as generative models, recommendation systems, and chatbots are becoming integral to web development, significantly simplifying and accelerating site creation. Generative models like ChatGPT and DALL-E enable developers to automatically generate textual and visual content, saving time and enhancing creative processes. Recommendation systems analyze user behavior and offer personalized solutions, improving user experience and increasing engagement. Chatbots used for automating user communication provide round-the-clock support and instant responses to frequently asked questions, greatly enhancing service levels. Additionally, AI assists in data analysis and content optimization for search engines, improving site visibility online. The article also discusses the advantages of applying AI in web development, including automation of routine tasks, improvement of content quality, and enhancement of marketing strategy effectiveness. In conclusion, it emphasizes that the integration of AI into web development opens new opportunities for businesses and creates more dynamic and adaptive web applications that meet user needs in the modern digital world.

Strategic information and its systemic processing in the space of current scientific discourse

Strategic information is a concept that has been known in management for a long time, but new ideas are constantly being born in the context of its systemic processing. In the following study, the thesis was adopted that the context of current conditions in the environment determines challenges in the field of systemic processing of strategic information. So what is the subject of scientific inquiry today and can any primary areas be identified? The aim of the article is to establish a general framework of the current scientific discourse on strategic information and its systemic processing based on a review of the latest scientific publications and the identification of key threads. In the research part, the method of systematic review of the literature on the subject using the resources of the EBSCO database was used. In the next step, the method of determining nomothetic binary oppositions was used. Such oppositions appear in a set of concepts that synthetically capture the content of individual texts. The procedure used showed that the current leading issues are related to: identifying strategic information resources in previously ignored areas of reality; revealing strategic potential through new information resources; modifying the scope and parameters of information considered strategic; changing the rules of the market game based on the innovative use of information revealing strategic importance.

Big Brother is Listening: Adapting and Marketing Literary Classics in Audio Format—The Case of Storytel’s 1984

Abstract The recent popularity of audiobooks has led to new ways of using the format as publishers and producers reproduce and “reactualise” older works by adapting them into audio. The article examines this development, focusing on the case of Storytel’s 2022 audio adaptation of George Orwell’s 1984 and the marketing campaign surrounding it, combining a publishing studies approach (Squires 2007; Murray 2018; Thompson 2021) with perspectives from materialist and sociological adaptation studies (Murray 2012) and audio narratology (Mildorf and Kinzel 2016; Bernaerts and Mildorf 2021). The article argues that the marketing campaign related to the 1984 production reflects the streaming service’s strategy of reviving the classic through sound. A paratextual analysis of the marketing material is combined with an analysis of the significance of sound and sound motifs in the adaptation and in the original novel. The article concludes that Storytel uses the audio format to reinterpret and highlight the relevance of the dystopic novel in 2022, while also using the classic to market Storytel as a streaming service in an Orwellian surveillance culture. By thus combining an interest in the textual and medial content of the work and the commercial and social contexts, the article demonstrates how developments in the audiobook industry impacts aesthetics as well as modes of producing, marketing and selling literature.

Exploring Metaheuristic Optimization Algorithms in the Context of Textual Cyberharassment: A Systematic Review

ABSTRACTThe digital landscape and rapid advancement of Information and Communication Technology have significantly increased social interactions, but it has also led to a rise in harmful behaviours such as offensive language, cyberbullying, and HS. Addressing online harassment is critical due to its severe consequences. This study offers a comprehensive evaluation of existing studies that employed metaheuristic optimization algorithms for detecting textual harassment content across social media platforms, highlighting their strengths and limitations. Using the PRISMA methodology, we reviewed and analysed 271 research papers, ultimately narrowing down the selection to 36 papers based on specific inclusion and exclusion criteria. By analysing key factors such as optimization techniques, feature engineering strategies, and dataset characteristics, we identify crucial trends and challenges in the field. Finally, we offer practical recommendations to improve the accuracy of predictive models, including adopting hybrid approaches, enhancing multilingual capabilities, and expanding models to operate effectively across various social media platforms.

Tasawuf Jawa: Kajian Filologis terhadap Naskah Suluk Ahya Ngulumodin

This research aims to describe the condition of Suluk Akya Ngulumodin manuscripts including: This research aims to describe the condition of the manuscript including: manuscript inventory, manuscript description and text study. The edited text uses a standard or critical edition accompanied by critical tools, and the translation of the text uses the following methods: literal, content, and free; Philological theory is used to reveal the cultural results contained in the manuscript. The object of this research is manuscript NB. 1768 of the National Library of Indonesia and BLK.0423-IS44 of the University of Indonesia Library. The results of this research are in the form of edits. Editing Suluk Akya Ngulumodin means preparing the manuscript ready to be published or printed. Editing in philology is the provision of a manuscript that is close to the original, which is a good and correct manuscript. Good means that it is easy to read and understand because it has been transcribed or transliterated (from Javanese script to Latin script) and the spelling has been adjusted to the target language. Correct means that it can be accounted for the validity of its text content, because it has been cleaned of writing errors through in-depth research.

Adaptive Recognition of English Translation Errors Based on Improved Machine Learning Methods

The accurate identification of errors in machine-translated English textual content is both necessary and tough, with massive implications for natural language processing packages. current device translation structures, while an increasing number of sophisticated, nevertheless fall prey to quite a number of errors that may compromise which means and fluency. This paper addresses these shortcomings through offering a greater system mastering method for the adaptive recognition of translation errors. We gift a singular framework that integrates the Transformer version, famed for its efficacy in shooting contextual relationships within text sequences, with a robust attention mechanism that prioritizes salient data at some stage in translation. The innovation of this studies lies in the incorporation of meta-getting to know techniques, allowing the model to self-modify in response to various classes of mistakes, therefore refining the precision of errors recognition. We outline the technique in detail, emphasizing the systematic steps taken to enrich the model’s adaptability. The efficacy of our approach is substantiated through comparative experiments, which demonstrate extremely good upgrades in errors identification over existing techniques. The results suggest the capacity of our adaptive mechanism to decorate the excellent of machine translation, paving the method for greater, reliable and nuanced language translation tools.

Tailoring glaucoma education using large language models: Addressing health disparities in patient comprehension.

This study evaluates the efficacy of GPT-4, a Large Language Model, in simplifying medical literature for enhancing patient comprehension in glaucoma care. GPT-4 was used to transform published abstracts from 3 glaucoma journals (n = 62) and patient education materials (Patient Educational Model [PEMs], n = 9) to a 5th-grade reading level. GPT-4 was also prompted to generate de novo educational outputs at 6 different education levels (5th Grade, 8th Grade, High School, Associate's, Bachelor's and Doctorate). Readability of both transformed and de novo materials was quantified using Flesch Kincaid Grade Level (FKGL) and Flesch Reading Ease (FKRE) Score. Latent semantic analysis (LSA) using cosine similarity was applied to assess content consistency in transformed materials. The transformation of abstracts resulted in FKGL decreasing by an average of 3.21 points (30%, P < .001) and FKRE increasing by 28.6 points (66%, P < .001). For PEMs, FKGL decreased by 2.38 points (28%, P = .0272) and FKRE increased by 12.14 points (19%, P = .0459). LSA revealed high semantic consistency, with an average cosine similarity of 0.861 across all abstracts and 0.937 for PEMs, signifying topical themes were quantitatively shown to be consistent. This study shows that GPT-4 effectively simplifies medical information about glaucoma, making it more accessible while maintaining textual content. The improved readability scores for both transformed materials and GPT-4 generated content demonstrate its usefulness in patient education across different educational levels.

Characteristics of suicide prevention apps: a content analysis of apps available in Canada and the UK

ObjectivesWe aimed to examine the characteristics, features and content of suicide prevention mobile apps available in app stores in Canada and the UK.DesignSuicide prevention apps were identified from Apple and...

Multi-layer access control for cloud data using improved DBSCAN, AES, homomorphic encryption, and RMAAC for text, image, and video

With the growing demand for cloud storage solutions that can handle diverse data types such as text, images, and videos, ensuring robust access control and security becomes important. This paper proposes a novel multi-layer access control framework for cloud environments, incorporating advanced Clustering Filtering Techniques with an Improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to enhance search efficiency across multiple data layers. This clustering approach enables quick and accurate retrieval of text, image, and video content by efficiently organizing data based on similarity. To ensure data privacy and security, employs a hybrid encryption approach, combining Advanced Encryption Standard (AES) for data at rest and Homomorphic Encryption (HE) for data in use, allowing secure data manipulation without compromising confidentiality. The access control mechanism is further strengthened by introducing a Role-based Multi-Attribute Access Control (RMAAC) model, which grants permissions based on a user’s role, attributes, and the sensitivity level of the data being accessed. This fine-grained control restricts unauthorized access while supporting flexible policies for different data types. Simulation results demonstrate that the proposed framework significantly improves data retrieval speed, security, and clustering performance, making it an effective solution for cloud storage systems handling diverse media formats.

Tackling misinformation in mobile social networks a BERT-LSTM approach for enhancing digital literacy

The rapid proliferation of mobile social networks has significantly accelerated the dissemination of misinformation, posing serious risks to social stability, public health, and democratic processes. Early detection of misinformation is essential yet challenging, particularly in contexts where initial content propagation lacks user feedback and engagement data. This study presents a novel hybrid model that combines Bidirectional Encoder Representations from Transformers (BERT) with Long Short-Term Memory (LSTM) networks to enhance the detection of misinformation using only textual content. Extensive evaluations revealed that the BERT-LSTM model achieved an accuracy of 93.51%, a recall of 91.96%, and an F1 score of 92.73% in identifying misinformation. A controlled user study with 100 participants demonstrated the model’s effectiveness as an educational tool, with the experimental group achieving 89.4% accuracy in misinformation detection compared to 74.2% in the control group, while showing increased confidence levels and reduced decision-making time. Beyond its technical efficacy, the model exhibits significant potential in fostering critical thinking skills necessary for digital literacy. The findings underscore the transformative potential of advanced AI techniques in addressing the challenges of misinformation in the digital age.

A Practice Report on Chinese-English Translation Based on Eco-TranslatologyTaking One Hundred Years, Many People, Many Stories as an Example

As a profound literary form that reflects individual life journeys and their historical contexts, oral autobiographies hold an irreplaceable position in the realm of Chinese literature. They serve not only as precious records of personal memories but also as vivid portrayals of historical transitions and social landscapes. In recent years, with the continuous development of translation studies, Eco-Translatology has emerged as an innovative and dynamic theoretical framework, becoming a focal topic within the field of translation research and attracting extensive scholarly attention and in-depth exploration. This theory emphasizes viewing translation as an interactive process within an ecosystem, focusing on the dynamic balance and harmonious coexistence among the source text, translator, target text, and the translation environment. This paper attempts to apply the core principles of Eco-Translatology to practical translation tasks, selecting the oral autobiography One Hundred Years, Many People, Many Stories by Yang Yi as the object of study. Based on a thorough understanding of the source texts content and the authors emotions and intentions, the paper conducts a comprehensive and in-depth analysis of the translation from three perspectives: linguistic, cultural, and communicative dimensions. Through this research, the aim is to provide a new perspective and methodological inspiration for the translation of autobiographical texts, promoting more diversified and integrated approaches in translation studies. Additionally, this study seeks to enrich and expand existing translation frameworks, offering valuable references for future translation practices

Effect of text message reminders to improve paediatric immunisation rates: a randomised controlled quality improvement project

Previous studies have demonstrated that text message reminders can improve pediatric vaccination rates, including low income & diverse settings such as those served by federally qualified health centers. In this...

Tour group prioritization driven by online reviews: using an improved EDAS-SIR method with credibility

Online reviews have become an important channel for people to share their experiences with tour groups. However, the complexity and uncertainty of the massive amount of online review information make it difficult for tourists to compare tour groups through personal browsing. Most studies usually focus on the semantic understanding of the review text content when extracting attributes, but they ignore the question of whether the review text content is credible. Therefore, based on 21,076 reviews of 6 tour groups from Ctrip.com, this paper proposes an online review-driven method considering credibility to assist tourists in choosing tour groups. Firstly, we use review credibility to measure the importance of words in reviews. Word2Vec and K-means are applied to cluster keywords and obtain attributes. Subsequently, sentiment analysis is conducted using Stanford CoreNLP. This analysis assesses the attitudes of reviewers towards each attribute, forming a nested probabilistic linguistic term set matrix to comprehensively retain the complex and multidimensional information present in the reviews. Finally, the EDAS-SIR method is proposed for ranking and selecting tour groups. Furthermore, comparative analysis with other methods demonstrates the good rationality and reliability of this method, which is applicable to other decision-making problems.

A textual data-driven method to measure the capabilities and core paths of different digital technologies to improve supply chain resilience

ABSTRACT This study expands current path research, which focuses on single digital technologies and singular types of mediating factors, by synthesising the findings from existing empirical, simulation, and textual content analyses to elucidate the multidimensional impact mechanisms of various digital technologies on SCR through a combination of static and dynamic path modelling. The results identify the most influential digital technologies and core pathways at different stages of research, revealing the dynamic evolutionary characteristics of the impact capabilities and pathways of digital technologies. Across all the research stages, blockchain technology (BCT) has the highest impact capability for improving SCR, SC disruption (NM-Dr) is the most important core negative mediating factor, and SC visibility (PM-Vis) is the most significant core positive mediating factor. The findings of this study assist supply chain managers across industries and related scholars in recognising the most suitable and potent digital technologies and the realisation path to enhance SCR.

Enhancing Persian text summarization through a three-phase fine-tuning and reinforcement learning approach with the mT5 transformer model

In the contemporary era, grappling with the vast expanse of big data presents a formidable obstacle, particularly when it comes to extracting vital information from extensive textual sources. The constant influx of news articles from various agencies necessitates an enormous amount of time to digest comprehensively. A viable solution to address this challenge lies in the realm of automatic text summarization, which is a pivotal and intricate endeavor within the field of natural language processing. Text summarization involves transforming pertinent textual content into a concise format that reduces its word count without compromising its underlying meaning. In recent years, transformers have emerged as a prominent force in the landscape of natural language processing, particularly in the realm of text summarization. This research endeavors to harness the power of transformers by training the mT5-base model on a three-step fine-tuning phase on Persian news articles. Subsequently, reinforcement learning via the PPO algorithm is integrated with the fine-tuned model. Finally, we evaluate the model’s performance in summarizing Persian texts, shedding light on its efficacy in addressing the formidable task of distilling meaningful insights from a sea of textual data. Our model has set a new benchmark in the field of Persian text summarization, achieving outstanding ROUGE scores of 53.17 for ROUGE-1, 37.12 for ROUGE-2, and 44.13 for ROUGE-L. These remarkable results reflect a significant advancement in the quality of Persian text summarization, signaling a promising era of more refined and context-aware summaries.

ازدواجية الجزاء عن انتهاك الحق في الصورة في ضوء القانون المغربي

The right to image is one of the most important rights that fall within the elements of private life. It has been subjected to serious violations today due to technological development and the wide spread of media and social media, which has prompted various comparative and national legislations to enact criminal and civil penalties related to the violation of this right. This research aims to discuss the criminal penalty for infringement of the right to image and compensation as a civil penalty for infringement in light of Moroccan law, based on an analytical and critical approach to the contents of legal texts and the national experience with foreign counterparts. It was found that the criminal sanction under Law No. 13-103 and the civil sanction under the Press and Publication Code constitute one of the main guarantees adopted by the national experience in order to establish the protection of the right to image by adopting a criminal and punitive policy while trying to reconcile individual rights and freedoms on the one hand and requiring the right of the state to punish on the other hand. By analyzing the contents of the texts regulating it, it is clear that there are still some points that make the protection of this right insufficient. The study recommends that the Moroccan national legislator, as well as comparative legislators, to intervene through legal reviews and amendments within the framework of the criminal code, represented by the draft Code of Criminal Procedure, and within the framework of the Civil Code, by drawing clear parameters of compensation to provide a wider scope of protection.

CAPTAIN: Capsule Network Integrated with Multilingual Transformer for Textual Abusive Content Detection in Online Social Networks

CAPTAIN: Capsule Network Integrated with Multilingual Transformer for Textual Abusive Content Detection in Online Social Networks