Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach by Olamilekan Shobayo, Sidikat Adeyemi-Longe, Olusogo Popoola, Bayode Ogunleye

“…This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. …”
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Evaluating Handwritten Answers Using DeepSeek: A Comparative Analysis of Deep Learning-Based Assessment by Sanskar Bansal, Vinay Gupta, Eshita Gupta, Peeyush Garg

“…The state-of-the-art technique known as Bidirectional Encoders Representation from Transformer (BERT) has overcome the drawbacks of previous NLP techniques like Bag of Words, TF-IDF, and Word2Vec. …”
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Sporting a virtual future: exploring sports and virtual reality patents using deep learning-based analysis by Jea Woog Lee, Sangmin Song, JungMin Yun, Doug Hyun Han, YoungBin Kim

“…Using patent big data, we introduce SportsBERT, a bidirectional encoder representation from transformers (BERT)-based algorithm tailored for enhanced natural language processing in sports-related knowledge-based documents. …”
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Rethinking Technological Investment and Cost-Benefit: A Software Requirements Dependency Extraction Case Study by Gouri Ginde, Guenther Ruhe, Chad Saunders

“…Specifically, we extract dependencies from textual descriptions of software requirements and analyze the performance of two state-of-the-art ML techniques: Random Forest and Bidirectional Encoder Representations from Transformers (BERT), a encoder only Large Language Model. …”
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Comparison of Deep Learning Sentiment Analysis Methods, Including LSTM and Machine Learning by Jean Max T. Habib, A. A. Poguda

“…In this case, it is crucial for researchers to explore the possibilities of updating certain tools, either to combine them or to develop them to adapt them to modern tasks in order to provide a clearer understanding of the results of their treatment. We present a comparison of several deep learning models, including convolutional neural networks, recurrent neural networks, and long-term and shortterm bidirectional memory, evaluated using different approaches to word integration, including Bidirectional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. …”
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Tackling misinformation in mobile social networks a BERT-LSTM approach for enhancing digital literacy by Jun Wang, Xiulai Wang, Airong Yu

“…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. …”
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Rumor detection using dual embeddings and text-based graph convolutional network by Barsha Pattanaik, Sourav Mandal, Rudra M. Tripathy, Arif Ahmed Sekh

“…This model uses dual embedding from two pre-trained transformer models: generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). …”
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ENHANCING NAMED ENTITY RECOGNITION ON HINER DATASET USING ADVANCED NLP TECHNIQUES by Harshvardhan Pardeshi, Prof. Piyush Pratap Singh

“…To solve this issue, some researchers have concentrated on NER models. Conversely, it lacks speed and accuracy. Therefore, the present research uses advanced NLP models such as bidirectional encoder representations from transformers (BERT), Distil BERT and the robustly optimized BERT approach (RoBERTA) for effective entity prediction performance. …”
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Optimizing an LSTM Self-Attention Architecture for Portuguese Sentiment Analysis Using a Genetic Algorithm by Daniel Parada, Alexandre Branco, Marcos Silva, Fábio Mendonça, Sheikh Mostafa, Fernando Morgado-Dias

“…To address this complexity, a discrete genetic algorithm was used to find an optimal configuration, selecting the layer types, placement of self-attention, dropout rate, and model dimensions and shape. A key outcome of this study was that the optimization process produced a model that is competitive with a Bidirectional Encoder Representation from Transformers (BERT) model retrained for Portuguese, which was used as the baseline. …”
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Graph neural networks embedded with domain knowledge for cyber threat intelligence entity and relationship mining by Gan Liu, Kai Lu, Saiqi Pi

“…Specifically, first, domain knowledge is collected to build a domain knowledge graph, which is then embedded using graph convolutional networks (GCN) to enhance the feature representation of threat intelligence text. Next, the features from domain knowledge graph embedding and those generated by the bidirectional encoder representations from transformers (BERT) model are fused using the Layernorm algorithm. …”
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Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms by Huong To Duong, Van Hai Ho, Phuc do

“…To address these challenges and advance fact-checking research both broadly and within the Vietnamese context, this paper introduces a fact-checking model tailored for Vietnamese, named ViKGFC. ViKGFC integrates a Knowledge Graph (KG), inference rules, and the Knowledge graph - Bidirectional Encoder Representations from Transformers (KG-BERT) deep learning model. …”
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Detecting Chinese Disinformation with Fine–Tuned BERT and Contextual Techniques by Lixin Yun, Sheng Yun, Haoran Xue

“…Building on large language models (LLMs) like BERT (Bidirectional Encoder Representations from Transformers) provides a promising avenue for addressing this challenge. …”
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Automated and efficient Bangla signboard detection, text extraction, and novel categorization method for underrepresented languages in smart cities by Tanmoy Mazumder, Fariha Nusrat, Abu Bakar Siddique Mahi, Jolekha Begum Brishty, Rashik Rahman, Tanjina Helaly

“…Finally, fine-tuning of the pre-trained multilingual Bidirectional Encoder Representations from Transformers (BERT) model is implemented to mimic human perception to achieve Named Entity Recognition (NER) capabilities. …”
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A Novel HDF-Based Data Compression and Integration Approach to Support BIM-GIS Practical Applications by Zeyu Pan, Jianyong Shi, Liu Jiang

“…Next, bidirectional transformation methods for BIM and GIS modeling data, images, and analytical data into HDF are proposed. …”
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Analysis of Short Texts Using Intelligent Clustering Methods by Jamalbek Tussupov, Akmaral Kassymova, Ayagoz Mukhanova, Assyl Bissengaliyeva, Zhanar Azhibekova, Moldir Yessenova, Zhanargul Abuova

“…This article presents a comprehensive review of short text clustering using state-of-the-art methods: Bidirectional Encoder Representations from Transformers (BERT), Term Frequency-Inverse Document Frequency (TF-IDF), and the novel hybrid method Latent Dirichlet Allocation + BERT + Autoencoder (LDA + BERT + AE). …”
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Empowering geoportals HCI with task-oriented chatbots through NLP and deep transfer learning by Mohammad H. Vahidnia

“…The notion of deep transfer learning (DTL) was then put into practice by customizing a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model for our particular aim and creating a task-oriented conversational agent. …”
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Enhancing medical response efficiency in real-time large crowd environments via smart coverage and deep learning for stable ecological health monitoring by Asma A. Alhashmi, Ghada Moh. Samir Elhessewi, Mukhtar Ghaleb, Nazir Ahmad, Nojood O. Aljehane, Tareq M. Alkhaldi, Hamad Almansour, Samah Al Zanin

“…Initially, the MRELC-SCHO model utilizes min-max normalization to transform the input data into a structured format. …”
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Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity by Abdulrahman Alzahrani, Patty Kostkova, Hamoud Alshammari, Safa Habibullah, Ahmed Alzahrani

“…CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. Bidirectional Encoder Representations from Transformers (BERT) allow multilingual users to interact and communicate properly in any linguistic environment. …”
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M.I.N.I.-KID interviews with adolescents: a corpus-based language analysis of adolescents with depressive disorders and the possibilities of continuation using Chat GPT by Irina Jarvers, Angelika Ecker, Pia Donabauer, Katharina Kampa, Maximilian Weißenbacher, Daniel Schleicher, Stephanie Kandsperger, Romuald Brunner, Bernd Ludwig

“…The transcribed interviews comprised 4,077 question-answer-pairs, with which we predicted the clinical rating (depressive/non-depressive) with use of a feedforward neural network that received BERT (Bidirectional Encoder Representations from Transformers) vectors of interviewer questions and patient answers as input. …”
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