Spellbound Kannada: Harnessing Conditional Generative Adversarial Networks for Transformative Word Suggestion Systems in Kannada Language Processing DOI Creative Commons

R J Prathibha,

N Anupama,

Anirudh Sudarshan

et al.

EAI Endorsed Transactions on Internet of Things, Journal Year: 2025, Volume and Issue: 11

Published: April 1, 2025

INTRODUCTION: The advancement of a word suggestion system model is driven by the need to enhance user interaction and efficiency in digital communication. Hence, helps minimize typographical errors spelling mistakes. Therefore, various traditional methods are used suggest words sentences; however, these models extremely time consuming, prone tedious. METHODS: Owing factors, present paper focuses on developing Kannada using cGAN (Conditional Generative Adversarial Networks), as this designed significantly offering predictive text suggestions language. RESULTS: training dataset, which resides AWS S3, comprises comprehensive collection texts utilized for both validation purposes. Furthermore, implementation leverages TensorFlow keras framework, specifically employing long short-term memory (LSTM) networks effective sequence prediction generation. LSTMs particularly advantageous NLP processing because they can capture long-term dependencies within sequential data. To facilitate interaction, web-based interface has been developed Flask, enabling users input initial characters receive dynamically generated suggestions. CONCLUSION: This not only delves into application cGANs realm but also illustrates practical deployment strategies utilizing cloud services modern web technologies. Overall, proposed approaches demonstrate potential enhancing experience through intelligent systems tailored

Language: Английский

Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection DOI Creative Commons
Alaa Mohasseb, Eslam Amer, Fatima Chiroma

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 856 - 856

Published: Jan. 16, 2025

Online misogyny is a significant societal challenge that reinforces gender inequalities and discourages women from engaging fully in digital spaces. Traditional moderation methods often fail to address the dynamic context-dependent nature of misogynistic language, making adaptive solutions essential. This study presents framework integrates advanced natural-language processing techniques with strategic data augmentation improve detection content. Key contributions include emoji decoding interpret symbolic communication, contextual expansion using Sentence-Transformer models, LDA-based topic modeling enhance richness understanding. The incorporates machine-learning, deep-learning, Transformer-based models handle complex nuanced language. Performance analysis highlights effectiveness selected comparative results emphasize transformative role augmentation. significantly enhanced model robustness, improved generalization, strengthened

Language: Английский

Citations

0
Textual sentiment classification in tourism research: between manual computing model and machine learning DOI
Yi Liu, Fangfei Han,

Lingkun Meng

et al.

Current Issues in Tourism, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: Jan. 17, 2025

Contemporary tourism scholars increasingly embrace artificial intelligent technologies, such as machine learning algorithms, for sentiment analysis. This paper presents a counterargument by introducing the Tourist Sentiment Evaluation (TSE) model through manual computing algorithms. By comparative experiment involving six models and TSE model, this demonstrates that can outperform selected approaches. The test relies on mixed dataset, comprising 244,974 online reviews multi-year questionnaire surveys from eight destinations in China. concludes approach retains distinctive advantages over AI approaches, due to accuracy, explanatory recursive applicability. Secondly, score ratings are unreliable because they do not match actual reviews. Thirdly, confirms existence of social positive bias context, known Pollyanna effect, where tourists exhibit propensity use words times more frequently than negative words. provides prevailing tendency adopt technologies various domains, affirming solid reliability computation. Additionally, utilisation holds significant potential overcoming linguistic barriers converting vast amounts Chinese texts into quantified scores.

Language: Английский

Citations

0
A systematic literature review on digital transformation in real estate: challenges and opportunities DOI
Wajhat Ali, Don Amila Sajeevan Samarasinghe, Zhenan Feng

et al.

Smart and Sustainable Built Environment, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

Purpose This study identifies key challenges to adopting smart real estate (SRE) technologies and offers insights recommendations enhance decision-making for stakeholders, including buyers property investors. Design/methodology/approach To achieve the aim of study, a rigorous research approach was employed, conducting an in-depth analysis 41 academic papers utilising PRISMA guidelines checklists. The chosen methodology also applies PEST (Political, Economic, Social Technological) framework identify factors influencing technology adoption in sector. Findings uncovers critical technologies, such as regulatory ambiguity, high implementation costs, societal resistance. reveals that unclear standards guidelines, coupled with financial burden implementation, are significant obstacles. Socially, resistance change difficulties integrating new prevalent. underscores potential artificial intelligence (AI) predictive analytics blockchain secure transactions records, though their is currently hindered by inadequate infrastructure challenges. These findings underscore need strategic interventions address these facilitate effective integration advanced sector, thereby enhancing industry innovation competitiveness. Practical implications stakeholders embrace effectively, conceptual contributing advancements. Originality/value study’s contribution offering execution tactics navigate utilise technology, driving

Language: Английский

Citations

0
Fostering Supportive Online Communities: Exploring Bystander Intervention in Cyberbullying Prevention DOI Creative Commons
Muhammad Shoaib,

Irshad Ahmed Abbasi

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

Abstract Cyberbullying can profoundly impact individuals' mental health, leading to increased feelings of anxiety, depression, and social isolation. Psychological research suggests that cyberbullying victims may experience long-term psychological consequences, including diminished self-esteem academic performance. The widespread use media platforms among university students has raised major concerns over cyberbullying, which have detrimental effects on student well-being We designed CBNet, a convolutional neural network (CNN)-based model for detecting groups. developed comprehensive dataset collected from several popular students. Our results demonstrate CBNet notably outperforms both the traditional machine learning approaches RNN-based presents an outstanding value precision, recall, F1-score overall, with Area Under ROC Curve significantly higher than 0.99. Combined fact issue always remains relevant, these suggest high feasibility our suggested approach detection incidents. Given results, could be used as preventative tool educators, administrators, community managers combat behavior make online safer more welcoming This work importance advanced real-world problems contributes creation greater digital in students’ communities. By employing institutions take proactive measures mitigate harmful cultivate positive culture conducive success flourishing.

Language: Английский

Citations

0
Sentiment Analysis of Product Reviews Using Fine-Tuned LLaMa-3 Model: Evaluation with Comprehensive Benchmark Metrics DOI Creative Commons
Yili Wang

ITM Web of Conferences, Journal Year: 2025, Volume and Issue: 70, P. 04021 - 04021

Published: Jan. 1, 2025

Sentiment analysis, a crucial subfield of natural language processing, enables businesses and policymakers to understand public emotions opinions, essential for crafting effective strategies across industries like marketing customer service. As the volume online reviews grows, automated sentiment classification models have become vital efficiently processing this data. This study explores fine-tuning LLaMA-8B large model based on Amazon Product Reviews dataset from Kaggle, aiming improve accuracy. Using LoRA approach combined with Variant Greedy Search Technique (VGST) TextBlob polarity handling, research addresses size challenges. The model’s process includes one-shot learning chain-of-thought prompting better capture nuanced expressions. Evaluated using comprehensive metrics, demonstrates superior precision compared Qwen2-7B achieves near LLaVA performance enhanced speed. Additionally, it outperforms Decision Tree, SVM, Multinomial NB, XLNet in work underscores potential analysis sets stage future extensions multimodal input scenarios.

Language: Английский

Citations

0
PREDICTING ACEDEMIC PERFORMANCE OF HIGHER EDUCATION STUDENTS BASED ON THEIR POTENTIAL INTENTION AND BEHAVIOUR ANALYSIS USING AI DOI

Palwinder Kaur Mangat,

S. Kaur

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Integrating media sentiment with traditional economic indicators: a study on PMI, CCI, and employment during COVID-19 period in Poland DOI Creative Commons
Iwona Kaczmarek, Adam Iwaniak, Grzegorz Chrobak

et al.

Journal of Computational Social Science, Journal Year: 2025, Volume and Issue: 8(2)

Published: Feb. 24, 2025

Language: Английский

Citations

0
Machine Learning for Quality Diagnostics: Insights into Consumer Electronics Evaluation DOI Open Access
Najada Firza,

Anisa Bakiu,

A. Monaco

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(5), P. 939 - 939

Published: Feb. 27, 2025

In the era of digital commerce, understanding consumer opinions has become crucial for businesses aiming to tailor their products and services effectively. This study investigates acoustic quality diagnostics latest generation AirPods. From this perspective, work examines sentiment using text mining analysis techniques applied product reviews, focusing on Amazon’s AirPods reviews. Using naïve Bayes classifier, a probabilistic machine learning approach grounded in Bayes’ theorem, research analyzes textual data classify reviews as positive or negative. Data were collected via web scraping, following ethical guidelines, preprocessed ensure relevance. Textual features transformed term frequency-inverse document frequency (TF-IDF) create input vectors classifier. The results reveal that provides satisfactory performance categorizing sentiment, with metrics such accuracy, sensitivity, specificity, F1-score offering insight into model’s effectiveness. Key findings highlight divergence perception across ratings, identifying drivers noise cancellation integration. These insights underline potential enabling companies address concerns, improve offerings, optimize business strategies. concludes methodologies are indispensable leveraging feedback rapidly evolving marketplace.

Language: Английский

Citations

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An Overview of Sentiment Analysis DOI
Fehmina Khalique, Neha Issar, Lakhwinder Kaur Dhillon

et al.

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 43 - 66

Published: March 6, 2025

Sentiment Analysis is a part of Data Intelligence Research that lays emphasis on data contains emotions. This analysis carried out by analysing the polarity content and thereby marking it as positive, negative, or neutral. In order to find how SA globally used, Supervised Natural Language Processing (SNLP) also utilised. this chapter, range these tools techniques will be discussed their applications elaborated. Additionally, chapter delve into further academic research related topic enhance understanding sentiment can support organizations in staying competitive boosting profits examining real-life examples. has experienced notable progress recent years, primarily propelled utilizing machine learning deep classification. helps building social political perceptions helping researchers policymakers understand public sentiments burning issues aiding decision-making an ever-changing digital world.

Language: Английский

Citations

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Harnessing Ethical AI DOI
Sheikh Inam Ul Mansoor, Showkat Ahmad Wani

Advances in social networking and online communities book series, Journal Year: 2025, Volume and Issue: unknown, P. 37 - 62

Published: Feb. 7, 2025

As communication technology advanced Social media came in as means and platform of deliberation connection wherein at the same time it opened doors for hate speech toxic content. In this chapter, author discusses moderation ethical AI today's social context that is increasingly becoming more polarized. Hate not only an issue some concern to users but also a global current society especially since affects harmony, human rights, freedom press thus need have anti-hate with strong backing. This Chapter focuses on concept Speech from legal, aspects within its effects individuals. It considers how circulates, works by analysing sophisticated methods include, functions procedure performed algorithms, processes such echo chambers, principles things going viral.

Language: Английский

Citations

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