Natural Language Processing Research Articles

Bayes Theorem in Machine Learning: A Literature Review

In the era of big data and articial intelligence, machine learning has become a crucial tool for extracting insights and making predictions across various domains. Bayes theorem, a fundamental principle in probability theory, has emerged as a cornerstone in many machine learning algorithms. This literature review explores the main applications of Bayes theorem in machine learning, focusing on its role in classication, Natural Language Processing (NLP), and other emerging elds. The study aims to provide an overview of how Bayesian principles enhance learning algorithms, improve decision-making processes, and address complex problems in articial intelligence. Through a systematic review of academic papers from Google Scholar, this research synthesizes current knowledge on Bayesian methods in machine learning. The methodology involves dening the research scope, conducting a literature search using specic keywords, screening relevant studies, and analyzing the collected data. By examining diverse applications ranging from disease prediction to sentiment analysis, this review highlights the versatility and signicance of Bayes theorem in advancing machine learning techniques and their real-world implementations.

Cultural Sensitivity in AI Language Learning: Using NLP to Enhance Language Understanding Across Cultural Contexts

The aim of this paper is to demonstrate the integration of cultural awareness into AI language learning systems using Natural Language Processing (NLP) models. Recognising that the traditional methods of language learning tend not to capture cultural variations, the studies focus on how culturally relevant NLP models (GPT-3, BERT, RNNs) promote language acquisition and cross-cultural integration. Data preprocessing, cultural data augmentation and model training was used to encode cultural variables such as greetings, politeness, and contextually relevant expressions into training datasets. Experimental results suggest that culturally sensitive models outperform the generic model on tasks that involve cultural awareness, such as politeness detection, cultural phrase recognition, and tone sensitivity. GPT-3 for example improved the accuracy of politeness detection tasks from 72.4% to 85.7%. These results highlight the need to bring cultural awareness into AI to develop engaging, context-sensitive language learning. It concludes that culturally based NLP models enhance learner capacity for real-world communication and foster global cultural awareness. Future work should focus on expanding culturally diverse datasets and refining AI models to address subtle cultural complexities

Sentiment Analysis Techniques for Deep Learning Classification and Comparison

Sentiment analysis, a critical tool in natural language processing (NLP), has seen significant advancements with the rise of machine learning and deep learning techniques. This paper explores the evolution of sentiment analysis from traditional models, such as CNN and RNN, to more sophisticated approaches like LSTM, LSTM-CNN, and BERT. Using the IMDb dataset for binary classification of sentiment, we utilize metrics like F-Score, Precision, Accuracy, and Recall to compare the way these models perform. Despite the progress made, sentiment analysis still faces challenges, particularly in handling multiple languages, incorporating multimedia content, and addressing privacy and ethical concerns. This paper also discusses future directions for sentiment analysis, including the development of models for low-resource languages, real-time sentiment analysis, and the ethical implications of data handling. Overall, the study highlights the potential of sentiment analysis and the opportunities for improvement through continued research and innovation.

Machine learning in ocular oncology and oculoplasty: Transforming diagnosis and treatment

In the domains of ocular oncology and oculoplasty, machine learning (ML) has become a game-changing technology, providing previously unheard-of levels of precision in diagnosis, treatment planning, and outcome prediction. Using imaging modalities, genomic data, and clinical characteristics, this chapter investigates the integration of machine learning algorithms in the detection and treatment of ocular tumours, including retinoblastoma and uveal melanoma. Through predictive modelling and real-time decision-making, it also emphasises how ML might improve surgical outcomes in oculoplasty, including orbital reconstruction and eyelid correction. Automated examination of fundus photographs, histological slides, and 3D imaging has been made possible by methods like deep learning and natural language processing, which have improved individualised therapeutic approaches and decreased diagnostic errors. Additionally, the use of augmented reality and machine learning in robotics and surgery is a significant development in precision oculoplasty. Notwithstanding its potential, issues including data heterogeneity, algorithm interpretability, and ethical considerations are significant roadblocks that need to be addressed. This chapter explores cutting-edge developments, real-world uses, and potential future paths, offering researchers and doctors a thorough resource.Dipali Vikas Mane, Associate Professor, Shriram Shikshan Sanstha’s College of Pharmacy, Paniv-413113

A clip-based dual-stream method for text based vehicle search

A text-based vehicle search refers to a system where users can find vehicles or route information by entering text-based queries. The primary objective of text-based vehicle search is to identify the most relevant vehicle in a given dataset using a natural language description as a query. This approach leverages natural language processing (NLP) to understand and interpret description queries and provide relevant results. Despite significant progress, this task still faces several challenges due to the complexity and diversity of natural language, as well as inherent difficulties in the vision domain. Moreover, few studies have focused on tracked-vehicle retrieval, where vehicle tracklets are considered instead of single images. In this paper, we propose a novel framework for natural language-based tracked-vehicle retrieval based on CLIP model, one of the most effective models for image-text matching task. This framework leverages both appearance and motion information to enhance the matching accuracy of vehicle tracklet retrieval. Some experiments are conducted on the CityFlow-NL dataset, provided by the 6-th AI City Challenge, an annual competition. The results are comparable to state-of-the-art methods, achieving an MRR score of 46.63%, Rank@5 of 67.02%, and Rank@10 of 81.82%

Guidelines for Gene and Genome Assembly Nomenclature.

The rapid increase in the number of reference-quality genome assemblies presents significant new opportunities for genomic research. However, the absence of standardized naming conventions for genome assemblies and annotations across datasets creates substantial challenges. Inconsistent naming hinders the identification of correct assemblies, complicates the integration of bioinformatics pipelines, and makes it difficult to link assemblies across multiple resources. To address this, we developed a specification for standardizing the naming of reference genome assemblies, to improve consistency across datasets and facilitate interoperability. This specification was created with FAIR (Findable, Accessible, Interoperable, and Reusable) practices in mind, ensuring that reference assemblies are easier to locate, access, and reuse across research communities. Additionally, it has been designed to comply with primary genomic data repositories, including members of the International Nucleotide Sequence Database Collaboration (INSDC) consortium, ensuring compatibility with widely used databases. While initially tailored to the agricultural genomics community, the specification is adaptable for use across different taxa. Widespread adoption of this standardized nomenclature would streamline assembly management, better enable cross-species analyses, and improve the reproducibility of research. It would also enhance natural language processing applications that depend on consistent reference assembly names in genomic literature, promoting greater integration and automated analysis of genomic data. This is a good time to consider more consistent genomic data nomenclature as many research communities and data resources are now finding themselves juggling multiple datasets from multiple data providers.

Characterizing the patient experience of physical restraint in psychiatric settings via a linguistic, sentiment, and metaphor analysis

Physical Restraint (PR) is a coercive procedure used in emergency psychiatric care to ensure safety in life-threatening situations. Because of its traumatic nature, studies emphasize the importance of considering the patient’s subjective experience. We pursued this aim by overcoming classic qualitative approaches and innovatively applying a multilayered semiautomated language analysis to a corpus of narratives about PR collected from 99 individuals across seven mental health services in Italy. Compared to a reference corpus, PR narratives were characterized by reduced fluency and lexical density, yet a greater use of emotional and cognitive terms, verbs, and first-person singular pronouns. Sadness was the most represented emotion, followed by anger and fear. One-third of the PR narratives contained at least one metaphor, with Animals and War/Prison as the most distinctive source domains. The quality and length of the PR experience impacted both the structure and the sentiment of the narratives. Findings confirm the distressful nature of PR but also point to the use of various linguistic mechanisms which might serve as an early adaptive response toward healing from the traumatic experience. Overall, the study highlights the importance of Natural Language Processing as an unobtrusive window into subjective experience, offering insights for therapeutic choices.

Race and Sex Disparities in Metabolic/Bariatric Surgery over 20 Years: A Cohort Study

Objectives: To determine how rates of discussion of metabolic and bariatric surgery (MBS) between patients with class 2 obesity and higher and their healthcare providers as well as subsequent progression to MBS have varied by race and sex over the last 20 years. Background: Obesity is a growing public health crisis. MBS remains the most effective treatment for long-term management of obesity. Methods: Retrospective cohort study of electronic health records from a large tertiary academic health system using a previously validated natural language processing algorithm. The primary outcomes were discussion of MBS between eligible patients and providers and progression to surgery. Multivariable logistic regression was used to determine the association between sociodemographic factors, time, and discussion and receipt of MBS. Results: Among 122,487 patients included in the analysis, 11,094 (9.1%) patients discussed MBS with their providers. Of these, 1348 (12.2%) progressed to MBS. Between 2000 and 2020 the annual fraction of patients who had a discussion of MBS with their providers rose from 3.2% to 10.0% (P < 0.001). Black people were equally likely to discuss MBS with providers as non-Black people (9.5 vs 9.0%, P = 0.09) but were less likely to progress to MBS (8.4 vs 12.6%, P < 0.001). These disparities in progression narrowed over the study period (P = 0.044). Men were less likely than women to both discuss (8.1% vs 9.6%, P < 0.001) and receive MBS (7.6% vs 14.6%, P < 0.001), and these disparities increased over the study period. Conclusions: Interventions to reduce racial disparities in utilization of MBS should focus on progression from discussion to MBS. Interventions to increase the uptake of MBS among men are required across the care cascade.

The Practice of Large Language Models in Automated Question Generation: A Case Study of ChatGLM in High School Information Technology Curriculum

With the advancement of information technology, the field of education is undergoing transformation. In the teaching of high school information technology, scientific and efficient question formulation is crucial for enhancing the quality of education. Traditional methods of question formulation rely on the experience of teachers, leading to issues such as inconsistent difficulty levels and incomplete coverage of knowledge points. Large language models (LLMs), such as ChatGLM, leverage natural language processing and deep learning technologies to automatically generate questions that align with the curriculum, thereby improving scientific accuracy and precision, enhancing diversity and innovation, and catering to students’ personalized needs. Additionally, LLMs can enhance the efficiency of question formulation and reduce the burden on teachers. This paper explores the application value of large language models in the formulation of questions for high school information technology, through empirical research comparing the performance of human-generated and ChatGLM-generated questions in terms of accuracy, relevance, clarity, and willingness. The study selected two chapters, “Data and Information” and “Fundamentals of Algorithms,” and employed both human and ChatGLM-generated questions, inviting teachers to evaluate them. Through data analysis and statistical testing, we reveal the advantages and limitations of large language models in educational question formulation, providing insights for the intelligent development of educational assessment systems.

Artificial Intelligence in Library Studies

Artificial intelligence has emerged as a promising technology in the post-pandemic era, significantly impacting the library ecosystem and the direction of library and information science studies. This study aims to map AI-related research in libraries to identify opportunities and discuss future directions. Using textual analysis of data from Scopus, the study analyzed article titles with the burst detection algorithm and abstracts with scattertext and lemmatization. Six burst words were detected out of twelve frequently appearing in titles. Scattertext results showed a comparison between the service side (red) and the development side (blue) in libraries. Research increasingly focuses on AI utilization for library services and natural language processing (NLP) to enhance services. On the development side, AI involves product creation and encompasses AI literacy frameworks, policies, and their impact on libraries. AI affects studies in libraries by changing application methods, such as machine learning and NLP. Future research will become more diverse, considering the unique characteristics of each library.

Virtual Banker at your Fingertips Using Chatbot System

The Virtual Banker systemThrough AI-Powered chatbots, real-time announcements, and video call support, it will ensure an extremely smooth and interactive banking experience for its users. The improvements in this system include a responsive user interface with integration of AI and APIs to provide dynamically interactive responses and backend secure connectivity to fetch data in real time. This also includes the features dynamic announcement management, live escalation on chat, analytics-driven improvements, and more. Scalable, optimized performance, and robust security, the system is capable of offering a comprehensive digital banking solution. The platform features interactive and AI-augmented learning tools to enhance the learning process. These tools include AI chatbots, word definition features, and content understanding mechanisms, allowing users to engage with complex concepts effectively. The integration of quizzes, Q&A sessions, and other testing formats reinforces learning and allows candidates to gauge their understanding of key topics. This paper examines the implementation and impact of chatbot systems in the banking sector. It highlights their ability to provide 24/7 customer service, streamline routine banking operations, and enhance user experiences. Key benefits include cost efficiency for banks, reduced wait times for customers, and improved operational scalability. However, challenges such as data security concerns, customer resistance to AI, and the need for continuous technological upgrades remain significant barriers to widespread adoption. Index Terms -Virtual banker, chatbot systems, artificial intelligence (AI) in banking, natural language processing (NLP), financial technology (FinTech), customer service automation, banking operations, transaction efficiency, digital banking solutions, machine learning in banking, AI-powered financial assistance, banking security and privacy, user experience in banking, predictive analytics in FinTech, and voice-enabled banking chatbots

An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks

The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs. While ANNs are relatively mature to construct intrusion detection models, some challenges remain. Among the most notorious of these are the bloated models caused by the large number of parameters, and the non-interpretability of the models. Our paper presents Convolutional Kolmogorov-Arnold Networks (CKANs), which are designed to overcome these difficulties and provide an interpretable and accurate intrusion detection model. Kolmogorov-Arnold Networks (KANs) are developed from the Kolmogorov-Arnold representation theorem. Meanwhile, CKAN incorporates a convolutional computational mechanism based on KAN. The model proposed in this paper is constructed by incorporating attention mechanisms into CKAN’s computational logic. The datasets CICIoT2023 and CICIoMT2024 were used for model training and validation. From the results of evaluating the performance indicators of the experiments, the intrusion detection model constructed based on CKANs has an attractive application prospect. As compared with other methods, the model can predict a much higher level of accuracy with significantly fewer parameters. However, it is not superior in terms of memory usage, execution speed and energy consumption.

The Impact of Linguistic Signals on Cognitive Change in Support Seekers in Online Mental Health Communities: Text Analysis and Empirical Study.

In online mental health communities, the interactions among members can significantly reduce their psychological distress and enhance their mental well-being. The overall quality of support from others varies due to differences in people's capacities to help others. This results in some support seekers' needs being met, while others remain unresolved. This study aimed to examine which characteristics of the comments posted to provide support can make support seekers feel better (ie, result in cognitive change). We used signaling theory to model the factors affecting cognitive change and used consulting strategies from the offline, face-to-face psychological counseling process to construct 6 characteristics: intimacy, emotional polarity, the use of first-person words, the use of future-tense words, specificity, and language style. Through text mining and natural language processing (NLP) technology, we identified linguistic features in online text and conducted an empirical analysis using 12,868 online mental health support reply data items from Zhihu to verify the effectiveness of those features. The findings showed that support comments are more likely to alter support seekers' cognitive processes if those comments have lower intimacy (βintimacy=-1.706, P<.001), higher positive emotional polarity (βemotional_polarity=.890, P<.001), lower specificity (βspecificity=-.018, P<.001), more first-person words (βfirst-person=.120, P<.001), more future- and present-tense words (βfuture-words=.301, P<.001), and fewer function words (βlinguistic_style=-.838, P<.001). The result is consistent with psychotherapists' psychotherapeutic strategy in offline counseling scenarios. Our research contributes to both theory and practice by proposing a model to reveal the factors that make support seekers feel better. The findings have significance for support providers. Additionally, our study offers pointers for managing and designing online communities for mental health.

MitraBotBot : Your one step guide towards all loans and schemes

Simply, the digital governance accelerates citizen-government service interaction more with simple tools. In the paper, a chatbot will be developed using web scraping techniques dynamically collecting and updating needed information from government websites based on eGovernance. This will facilitate meaningful, intuitive conversations between users and the language model, Llama 3.2, making the access to public data and services even more interesting. All this will be collected in real time by the web scraper: knowledge base of the chatbot, including policies issued, deadlines covered by the government, citizen services offered, and many more. Inserting language comprehension and conversational capabilities of Llama 3.2 with gathering data in real time would be expected to create ease of interaction for the user, with minimum delay in information, and a realistic answer to citizens' inquiries. Discussions also include issues when web scraping on public websites is applied, legal perspectives, and efficiency in the integration of large language models into eGovernance systems. Results are readily available, responsive, and satisfactory concerning user satisfaction for AI-powered government service chatbots. Index Terms-Chatbot, eGovernance, Llama 3.2, Natural Language Processing

Artificial intelligence contributes to the creative transformation and innovative development of traditional Chinese culture

In recent years, artificial intelligence (AI) has emerged as a transformative force in various fields, including the arts and culture. This is particularly evident in the context of traditional Chinese culture, where AI has become a powerful tool in its creative transformation and innovative development. With its advanced capabilities in data processing and generating new ideas, AI is not only helping to preserve the rich heritage of Chinese culture but is also playing a crucial role in its evolution. This study aims to delve into how AI is reshaping the traditional elements of Chinese culture, such as calligraphy, Chinese paintings and traditional artworks, and assess its impact on both conservation and modern reinterpretation. We also examine real-world applications and projects that utilize AI technologies, such as machine learning, natural language processing, and computer vision. Our findings indicate that AI's contribution to traditional Chinese culture is multifaceted. One of the key areas where AI has made a significant impact is in the preservation and restoration of cultural artifacts. AI algorithms have demonstrated remarkable proficiency in analyzing large datasets of historical texts and artworks, uncovering previously unknown patterns and facilitating the restoration of ancient texts and relics. The integration of artificial intelligence into the realm of traditional Chinese culture signifies a pivotal moment in its history. AI's role extends beyond mere preservation; it is a catalyst for innovation, fostering new forms of artistic expression and promoting a dynamic cross-cultural exchange. As AI technology continues to evolve, it is expected to further revolutionize the way we interact with and understand traditional Chinese culture, opening up new avenues for creative exploration and cultural dialogue. This study underscores the potential of AI as a tool for cultural enrichment and highlights the exciting prospects for future developments in this area.

Clinical entity-aware domain adaptation in low resource setting for inflammatory bowel disease

The digitization of healthcare records has revolutionized medical research and patient care, with electronic health records (EHRs) containing a wealth of structured and unstructured data. Extracting valuable information from unstructured clinical text presents a significant challenge, necessitating automated tools for efficient data mining. Natural language processing (NLP) methods have been pivotal in this endeavor, aiming to extract crucial clinical concepts embedded within free-form text. Our research addresses the imperative for robust biomedical entity extraction, focusing specifically on inflammatory bowel disease (IBD). Leveraging novel domain-specific pre-training and entity-aware masking strategies with contrastive learning, we fine-tune and adapt a general language model to be better adapted to IBD-related information extraction scenarios. Our named entity recognition (NER) tool streamlines the retrieval process, supporting annotation, correction, and visualization functionalities. In summary, we developed a comprehensive pipeline for clinical Dutch NER encompassing an efficient domain adaptation strategy with domain-aware masking and model fine-tuning enhancements, and an end-to-end entity extraction tool, significantly advancing medical record curation and clinical workflows.

Enhancing Privacy While Preserving Context in Text Transformations by Large Language Models

Data security is a critical concern for Internet users, primarily as more people rely on social networks and online tools daily. Despite the convenience, many users are unaware of the risks posed to their sensitive and personal data. This study addresses this issue by presenting a comprehensive solution to prevent personal data leakage using online tools. We developed a conceptual solution that enhances user privacy by identifying and anonymizing named entity classes representing sensitive data while maintaining the original context by swapping source entities for functional data. Our approach utilizes natural language processing methods, combining machine learning tools such as MITIE and spaCy with rule-based text analysis. We employed regular expressions and large language models to anonymize text, preserving its context for further processing or enabling restoration to the original form after transformations. The results demonstrate the effectiveness of our custom-trained models, achieving an F1 score of 0.8292. Additionally, the proposed algorithms successfully preserved context in approximately 93.23% of test cases, indicating a promising solution for secure data handling in online environments.

ChatGPT, Python, and Microsoft Excel

This article examines how the integration of ChatGPT, Python, and Microsoft Excel can significantly enhance the productivity of medical librarians. These tools provide a powerful framework for automating tasks, processing large datasets, and generating actionable insights. ChatGPT, with its advanced natural language processing, helps automate Python script generation and improve data processing in Excel, streamlining workflows and enabling more precise service delivery. Practical applications demonstrate improvements in information retrieval, data management, and research support. The results show that, despite some integration challenges, these technologies have the potential to greatly elevate the efficiency and effectiveness of librarians in managing healthcare data.

Natural Language Processing for Dialects of a Language: A Survey

State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectal datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches. We describe a wide range of NLP tasks in terms of two categories: natural language understanding (NLU) (for tasks such as dialect classification, sentiment analysis, parsing, and NLU benchmarks) and natural language generation (NLG) (for summarisation, machine translation, and dialogue systems). The survey is also broad in its coverage of languages which include English, Arabic, German, among others. We observe that past work in NLP concerning dialects goes deeper than mere dialect classification, and extends to several NLU and NLG tasks. For these tasks, we describe classical machine learning using statistical models, along with the recent deep learning-based approaches based on pre-trained language models. We expect that this survey will be useful to NLP researchers interested in building equitable language technologies by rethinking LLM benchmarks and model architectures.

Natural language processing to identify suicidal ideation and anhedonia in major depressive disorder.

Anhedonia and suicidal ideation are symptoms of major depressive disorder (MDD) that are not regularly captured in structured scales but may be captured in unstructured clinical notes. Natural language processing (NLP) techniques may be used to extract longitudinal data on suicidal behaviors and anhedonia within unstructured clinical notes. This study assessed the accuracy of using NLP techniques on electronic health records (EHRs) to identify these symptoms among patients with MDD. EHR-derived, de-identified data were used from the NeuroBlu Database (version 23R1), a longitudinal behavioral health real-world database. Mental health clinicians annotated instances of anhedonia and suicidal symptoms in clinical notes creating a ground truth. Interrater reliability (IRR) was calculated using Krippendorff's alpha. A novel transformer architecture-based NLP model was trained on clinical notes to recognize linguistic patterns and contextual cues. Each sentence was categorized into one of four labels: (1) anhedonia; (2) suicidal ideation without intent or plan; (3) suicidal ideation with intent or plan; (4) absence of suicidal ideation or anhedonia. The model was assessed using positive predictive values (PPV), negative predictive values, sensitivity, specificity, F1-score, and AUROC. The model was trained, tested, and validated on 2,198, 1,247, and 1,016 distinct clinical notes, respectively. IRR was 0.80. For anhedonia, suicidal ideation with intent or plan, and suicidal ideation without intent or plan the model achieved a PPV of 0.98, 0.93, and 0.87, an F1-score of 0.98, 0.91, and 0.89 during training and a PPV of 0.99, 0.95, and 0.87 and F1-score of 0.99, 0.95, and 0.89 during validation. NLP techniques can leverage contextual information in EHRs to identify anhedonia and suicidal symptoms in patients with MDD. Integrating structured and unstructured data offers a comprehensive view of MDD's trajectory, helping healthcare providers deliver timely, effective interventions. Addressing current limitations will further enhance NLP models, enabling more accurate extraction of critical clinical features and supporting personalized, proactive mental health care.