From Words to Ratings: Machine Learning and NLP for Wine Reviews by Iliana Ilieva, Margarita Terziyska, Teofana Dimitrova

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Evaluation of StyleGAN-CLIP Models in Text-to-Image Generation of Faces by Asma Fejjari, Aaron Abela, Marc Tanti, Adrian Muscat

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Domain‐Specific Customization for Language Models in Otolaryngology: The ENT GPT Assistant by Brenton T. Bicknell, Nicholas J. Rivers, Adam Skelton, Delaney Sheehan, Charis Hodges, Stevan C. Fairburn, Benjamin J. Greene, Bharat Panuganti

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Artificial Intelligence in Academic Translation: A Comparative Study of Large Language Models and Google Translate by Мохаммед Мохсен

“…This motivated this study to conduct a rigorous evaluation of the effectiveness and precision of machine translation, represented by Google Translate (GT), in comparison to Large Language Models (LLMs), specifically ChatGPT 3.5 and 4, when translating academic abstracts bidirectionally between English and Arabic. …”
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TriPlaNet: Enhancing machine-paraphrasing plagiarism detection through triplet network and contrastive learning by Deyu Meng, Ziheng Wang, Tshewang Phuntsho, Tad Gonsalves

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Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions by Nora Darjazini, Mohammad Hossein Zarghami, Reza Ghorban Jahromi, Leila Shobeiry

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Real-Time Norwegian Sign Language Recognition Using MediaPipe and LSTM by Md. Zia Uddin, Costas Boletsis, Pål Rudshavn

“…The application of machine learning models for sign language recognition (SLR) is a well-researched topic. …”
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Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks by Pathomthat Chiradeja, Yijuan Liang, Chaiyan Jettanasen

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A semi supervised framework for human and machine collaboration in computer assisted text refinement by Yicheng Sun, Yi Wang, Hanbo Yang, Richard Suen

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Who Will Author the Synthetic Texts? Evoking Multiple Personas from Large Language Models to Represent Users’ Associative Thesauri by Maxim Bakaev, Svetlana Gorovaia, Olga Mitrofanova

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Psychotherapist remarks’ ML classifier: insights from LLM and topic modeling application by Alexander Vanin, Vadim Bolshev, Anastasia Panfilova

“…The trained classifier demonstrates robust performance in distinguishing these thematic patterns.DiscussionThe study shows that automated topic modeling, combined with expert input, can effectively uncover how therapist language patterns emerge and persist across different therapeutic styles. …”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…The blending ensemble technique with the XGBoost meta-learning model accomplished the best performance for learning style detection with an accuracy of 97.56%. Next, the text content of the electronic documents is modified by employing different natural language processing (NLP) techniques, including named entity recognition of spaCy, knowledge graph, generative pre-trained transformer 3 (GPT-3), and text-to-text transfer transformer (T5) model, to accommodate diverse learning styles. …”
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The Code and the Prospects of Significance in the Poem (The stranger) Wafeek Sleiteen by Zakwan Alabdo

“…This research comes to provide an analytical insight into the poem (The Stranger)  by Wafiq Sulaitan, in an attempt to study his style of poetry, and to shed the light on the code, and its effectiveness in the production of significance with interest in what recovered by the code of mystical, existential, legendary dimensions. …”
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Quality and Machine Translation: A realistic objective? by Rebecca Fiederer Sharon O'Brien

“…Eleven suitably qualified raters rated 30 source sentences, three translated versions and three post-edited versions for the parameters clarity, accuracy and style. The results show that the machine translated, post-edited output was judged to be of higher clarity and accuracy, while the translations were judged to be of better style. …”
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Artificial Intelligence Applied to the Analysis of Biblical Scriptures: A Systematic Review by Bruno Cesar Lima, Nizam Omar, Israel Avansi, Leandro Nunes de Castro

“…The main challenges in the field involve the nature and style of the language used in the Bible, among others.…”
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Large Language Models for Synthetic Dataset Generation of Cybersecurity Indicators of Compromise by Ashwaq Almorjan, Mohammed Basheri, Miada Almasre

“…Therefore, this study aims to fine-tune OpenAI’s Large Language Model (LLM), Gpt-3.5, to generate a synthetic dataset that replicates the style of a real social media curated dataset, as well as incorporates select IoCs as domain knowledge. …”
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Special Issue on Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis by Silvia García-Méndez, Francisco de Arriba-Pérez, Enrique Costa-Montenegro

“…In recent years, natural language processing (<span style="font-variant: small-caps;">nlp</span>) has undergone a profound transformation driven by significant advances in artificial intelligence, machine learning, and the massive availability of linguistic data [...]…”
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Prompt-based fine-tuning with multilingual transformers for language-independent sentiment analysis by Faizad Ullah, Safiullah Faizullah, Imdad Ullah Khan, Turki Alghamdi, Toqeer Ali Syed, Ahmad B. Alkhodre, Muhammad Sohaib Ayub, Asim Karim

“…The key contribution lies in the implementation of prompt-based fine-tuning strategies for language independent sentiment analysis. Using (1) prefix prompts and (2) cloze-style prompts, a unified framework is established that employs templates designed in one language and evaluates their performance on data from the remaining $$(n-1)$$ ( n - 1 ) languages. …”
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CKSD: Comprehensive Kurdish-Sorani database by Jihad Anwar Qadir, Samer Kais Jameel, Wshyar Omar Khudhur, Kamaran H. Manguri

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Design and Implementation of a Chatbot for Kurdish Language Speakers Using Chatfuel Platform by Hemn Karim Ahmed, Jamal Ali Hussein

“…Chatbot is a software agent that is used to conduct intelligent conversations between machines and humans. Chatbots are mostly depend on Natural Language Processing (NLP). …”
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