This research explores the connection between interventions for mental well-being and Virtual Reality (VR).A bibliometric analysis was conducted to assess the state of the field, comparing mental health applications to understand VR's significance.The study revealed a limited but growing body of literature examining VR's effects on mental health, primarily targeting mood, stress, and anxiety disorders.Various approaches, including immersive VR experiences, were identified, offering unique therapeutic benefits.The comparative investigation across disorders underscored the potential of VR therapy to create synergistic effects when combined with other interventions.Immersive VR experiences were found to offer innovative ways to address emotional regulation and stress management, enhancing traditional therapeutic methods.The applications and techniques developed so far provide significant insights into the transformative role VR could play in mental health care.The findings emphasize the importance of further research to optimize and expand VR interventions for mental well-being.Such advancements could pave the way for more personalized, engaging, and effective mental health solutions, particularly for conditions resistant to conventional therapies.By leveraging VR's immersive and interactive capabilities, mental health practitioners can create interventions that not only alleviate symptoms but also foster long-term psychological resilience.This study highlights the critical need to develop and implement VR-based interventions systematically, ensuring their accessibility and efficacy across diverse populations.By doing so, VR can serve as a cornerstone in the evolution of mental health care, bridging gaps and unlocking new possibilities for well-being.

Traditional educational institutions prioritize theoretical education over hands-on practical skills which produce a gap between classroom learning and industry requirements particularly in the fast-growing blockchain sector.A structured case study demonstrates how Project-Based Learning (PBL) was implemented in an undergraduate engineering course which focused on blockchain technology.The educational approach evolved through four stages that combined theoretical instruction with collaborative solution creation and DApp programming and assessment evaluation.Performance metrics from students including testing coverage, GitHub contributions, documentation quality and research paper output are carefully analyzed through algorithmic guidance of each phase.The paper demonstrates the development of teaching methods through traditional practices and outcome-based instruction up to project-based learning supported by visual timeline comparisons.Student feedback demonstrates that the education methods led to enhanced technical abilities together with teamworking and increased student confidence.The case study demonstrates how PBL functions as an educational connection between academic learning and practical blockchain development needs because most teams (over 75%) finished functional DApps alongside several groups producing research suitable for publication.

Sarcasm, a subtle form of expression, is challenging to detect, especially in modern communication platforms where communication transcends text to encompass videos, images, and audio.Traditional sarcasm detection methods rely solely on textual data and often struggle to capture the nuanced emotional inconsistencies inherent in sarcastic remarks.To overcome these shortcomings, this paper introduces a novel multimodal framework incorporating text, audio, and emoji data for more effective sarcasm detection and emotion classification.A key component of this framework is the Contextualized Semantic Self-Guided BERT (CS-SGBERT) model, which generates efficient word embeddings.Primarily, frequency spectral analysis is performed on the audio data, followed by preprocessing and feature extraction, while text data undergoes preprocessing to extract lexicon and irony features.Meanwhile, emojis are analyzed for polarity scores, which provide a rich set of multimodal features.The fused features are then optimized using the Camberra-based Dingo Optimization Algorithm (C-DOA).The selected features and the embedded words from the preprocessed texts are given to Entropy-based Robust Scaling -Gated Recurrent Units (E-RS-GRU) for detecting sarcasm.Experimental results on the MUStARD dataset show that the proposed E-RS-GRU model achieves an accuracy of 76.65% and F1-score of 76.9%, with a relative improvement of 2.18% over the best-performing baseline and 1.25% over the best-performing state-of-the-art model.Additionally, KLKI-Fuzzy model is proposed for emotion recognition, which dynamically adjusts membership functions through Kullback-Leibler Kriging Interpolation (KLKI), enhancing emotion classification by processing features from all modalities.The KLKI-Fuzzy model exhibits enhanced emotion recognition performance with reduced fuzzification and defuzzification times.

The classification of scientific articles faces challenges due to the complexity and diversity of academic content.In response to this issue, a new approach is proposed, utilizing Ensemble Learning, specifically Decision Tree, Random Forest, AdaBoost, and XGBoost, along with Convolutional Neural Network (CNN) techniques.This study utilizes the arXiv dataset, comparing the effectiveness of Term Frequency-Inverse Document Frequency (TFIDF) and Sentence-BERT (SBERT) for text representation.To further refine feature extraction, vectors derived from SBERT are integrated into the CNN framework for dimensionality reduction and obtaining more representative feature maps named latent feature vectors.The study also observes the impact of incorporating both the title and abstract on performance, demonstrating that richer textual information enhances model accuracy.The hybrid model (CNN + Ensemble Learning) demonstrates a substantial improvement in classification accuracy compared to traditional Ensemble Learning.The evaluation shows that CNN + SBERT with XGBoost achieved the highest accuracy of 94.62%, showcasing the benefits of combining advanced feature extraction techniques with powerful models.This research emphasizes the potential of integrating CNN within the Ensemble Learning paradigm to enhance the performance of scientific article classification and provides insights into the crucial role of CNN in improving model accuracy.Additionally, the study highlights the superior performance of SBERT in feature extraction, contributing beneficially to the overall model.

The problem of this research is how to overcome the need for Maritime English textbooks that integrate English language skills (reading, writing, speaking and listening).This research aims to develop a valid, practical and effective English textbook to improve students' understanding of English in the maritime field.The research design uses the Research and Development (R&D) method.This research was conducted at the Banyuwangi Maritime

Rapid progress in generative artificial intelligence (AI) technologies has brought forth stupendous challenges in differentiating AI-written text from human text.The Naturalness Score, a composite measure that considers lexical diversity, syntactic complexity, sentiment variability, and grammatical faults, is a new idea that emerged from this study.The Naturalness score is part of a larger machine learning framework, although it does have an individual classifier called the Naturalness-Based Logistic Regression Classifier or NLRC.The NLRC model was analyzed against a large, diverse corpus of nearly 45,000 text samples, most of which were student essays, articles, and web-scraped content.The proposed model outperformed all existing baseline models with an accuracy of 96.41%, precision of 0.98, recall of 0.95, and F1 score of 0.96.The high areas under the receiver operating characteristic curve (AUC=1.00)and precision-recall curve (AUC-PR) also indicate the effectiveness of the model in differentiating AI generated from human-written text.Volume 17 (2025), Issue 3The proposed approach offers several advantages including increased detection accuracy, resilience against AIgenerated content, cross-domain applicability, and interpretability.The research has implications for applying such models in schools, although it also calls for future research on the implications of the rapidly changing landscape of AIgenerated content which it states.It emphasizes the importance of these findings in developing robust and adaptive detection systems to ensure the integrity of academic assessments, thereby preventing the misuse of AI tools.

In agile development, user stories are the primary method for defining requirements.These days, managing user stories effectively is difficult because software projects typically contain a large number of them.A project can involve a large amount of user stories, which should be clustered into different groups based on their functionality's similarity for systematic requirements analysis, effective mapping to developed features, and efficient maintenance.Unfortunately, the majority of user story clustering methods now in use require a great deal of manual work, which is error-prone and time-consuming.In this research, we suggest an automated framework that uses a family of machine learning algorithms to classify user stories.First, preprocessing the data is done in order to examine user stories and extract keywords from them.After that, features are taken out, which allow user stories to be automatically grouped into distinct categories.We use four feature extraction algorithms and six clustering algorithms.According to our experimental results, K-means and BIRCH clustering outperform other clustering methods, whereas cosine similarity and distance are the best feature extraction for user stories categorization to form the more balanced clusters as they both have the standard deviation is 3.08.In case of user stories cohesion, the silhouette coefficient value is 0.225 for spectral with (cosine similarity and cosine distance feature extraction) is best outcome than other clustering algorithms.The usefulness and applicability of the suggested framework are demonstrated by this study.Additionally, it offers some useful recommendations for enhancing the effectiveness of user stories clustering, for example through parameter adjustments for enhanced feature extraction and clustering.

Traditional distance learning has been widely adopted for its capacity to provide educational access to a broad and diverse audience, overcoming spatial and temporal limitations.However, it needs to deliver the same immersion and learning effectiveness as face-to-face instruction, particularly in courses requiring hands-on practice, where these limitations become more pronounced.To address this, virtual reality (VR)-based distance learning has gained attention as a potential solution.Previous studies have confirmed that VR-enhanced distance learning can improve educational outcomes; however, a standardized teaching-learning model designed explicitly for VR-based distance learning has yet to be established.Consequently, instructors have often relied on conventional models, leading to variability in instructional quality.This paper proposes the VR IGLOO model, a structured VR-based teachinglearning framework tailored for distance education.For this purpose, analysis of the conventional studies and focus group interview (FGI) of the expert group were conducted.And the validity of the proposed model was verified through Delphi validation.