Language Understanding Research Articles

Borich 요구도와 The Locus for Focus model을 활용한 대학생의 챗GPT 현 수준 및 중요도 분석

Objectives The purpose of this study is to analyze the perception level of university students regarding ChatGPT using the Borich Needs and The Locus for Focus model. Methods For this purpose, a total of 199 undergraduate students from University A in the metropolitan area were selected as participants, and the research was conducted using SPSS 25.0 and Excel software programs. Statistical significance was analyzed using t-tests to examine the differences between the current level and im-portance in IPA analysis.Borich'sNeeds and The Locus for Focus Model were examined in a 2x2 matrix, divided into four quadrants. Results The key findings of this study are as follows: First, in the IPA analysis, the reinforcement and maintenance area(HH) revealed 2 items related to understanding ChatGPT, 5items related to ChatGPT principles and applica-tions, and 1 item related to data and machine learning. The concentration management area(HL) included 1 item related to ChatGPT principles and applications, and 3 items related to data and machine learning. The low priority area(LL) consisted of 1 item related to understanding ChatGPT, 2 items related to ChatGPT principles and applica-tions, and 4 items related to social impact. The excessive area(LH) was characterized bybiaseddata in the social impact category. Second, it was found that the differences between importance and current level were higher in terms of importance for all items, except for those related to data bias. This difference was statistically significant. The priority order according to Borich's needs includes 'structuredand unstructured data', 'sensorsand perception', 'computer vision', 'concepts of AI agents' and 'conceptsand application learning of machines'. Third, The Locus for Focus model analysis revealed that the HH area consisted of 9 items, including 'speech recognition and language understanding', 'problem solving and exploration', 'representation and deduction of information', 'concepts and ap-plication learning of machines', 'concepts and application of artificial neural networks', 'data attributes', 'structured and unstructured data', 'classification models', and 'machine learning model implementation'. Fourth, a total of 6 items, including 'speech recognition and language understanding', 'representation and deduction of information', 'concepts and application learning of machines', 'concepts and application of artificial neural networks', 'structured and unstructured data', and 'classification models' were consistently rated as high priority and demanded the high-est attention. Conclusions The findings of this study indicate the need to explore ways to apply ChatGPT in education. Furthermore, it proposes a long-term education plan to enhance awareness of the social impact of ChatGPT and provide practical strategies for learning and applying it.

Virtual Assistant Using Python

Today there is huge Advancement in the Technical field which is increasing day by day. In early days there were only computer systems where we were able to perform only few tasks, but today new technologies like machine learning, artificial intelligence, deep learning, and few some others have made computer systems so advance that we can perform any type of task with them. In recent years, Artificial Intelligence (AI) have done remarkable progress and its Capability is increasing day by day. One of the application Area of AI is Natural Language Processing (NLP). Natural Language Processing (NLP) helps Humans to communicate with the computer system in their own Language. For example, Voice Assistant. Various voice assistants were developed and they are still being improved more for better performance to overcome struggling of humans to interact with their machine. we are trying to develop a voice assistant using python which will help user to perform any type of task without interaction with keyboard. The aim of this paper is to study how voice assistants behaves smartly and can be used to get everyday work done and also be used for educational purpose also. Keywords: Virtual Assistant, UI, Artificial Intelligent, Python Library Key Features of the system are: ❖ Natural Language Understanding (NLU) ❖ Speech Recognition ❖ Text-to-Speech (TTS) Conversion ❖ User Interface ❖ Context Management

Unit Test Generation Using Large Language Models: A Systematic Literature Review

Unit Test Generation Using Large Language Models: A Systematic Literature Review

SSR: Solving Named Entity Recognition Problems via a Single-stream Reasoner

Information Extraction (IE) focuses on transforming unstructured data into structured knowledge, of which Named Entity Recognition (NER) is a fundamental component. In the realm of Information Retrieval (IR), effectively recognizing entities can substantially enhance the precision of search and recommendation systems. Existing methods frame NER as a sequence labeling task, which requires extra data and, therefore may be limited in terms of sustainability. One promising solution is to employ a Machine Reading Comprehension (MRC) approach for NER tasks, thereby eliminating the dependence on additional data. This process encounters key challenges, including: (1) Unconventional predictions; (2) Inefficient multi-stream processing; (3) Absence of a proficient reasoning strategy. To this end, we present the Single-Stream Reasoner (SSR), a solution utilizing a reasoning strategy and standardized inputs. This yields a type-agnostic solution for both flat and nested NER tasks, without the need for additional data. On ten NER benchmarks, SSR achieved state-of-the-art results, highlighting its robustness. Furthermore, we illustrated its efficiency through convergence, inference speed, and low-resource scenario performance comparisons. Our architecture displays adaptability and can effortlessly merge with various foundational models and reasoning strategies, fostering advancements in both the IR and IE fields.

“What Scares Me Is the Speed at Which Artificial Intelligence Is Developing”: Students' Perceptions of Artificial Intelligence in Foreign Language Teaching

As artificial intelligence (AI) becomes an integral part of our daily lives, the concern of the teaching community about the illegal use of these technologies in the educational process is increasing. In order to adapt the education system and teaching practices to new technological challenges, it is necessary to analyze the opinions of all the parties concerned. The purpose of this study is to identify the attitude of students of Kazan Federal University to the use of artificial intelligence technologies in the educational process and the practice of their application in foreign languages learning process. To achieve this goal, an online survey of students of Kazan Federal University was conducted. The survey touched upon the practical aspects of the use of artificial intelligence in language teaching, the advantages and disadvantages of AI tools from the students’ point of view, as well as their opinion regarding the prospects of AI in education. As a result of the study, we came to the conclusion that at the moment AI tools are not widespread enough in teaching foreign languages. Only one-fifth of the respondents use these tools, but the respondents’ comments suggest that the number of users will grow. The attitude of students towards the use of AI is ambiguous, with responses ranging from enthusiastic to skeptical. Students’ positive impressions are mainly related to saving time and effort, as well as to the ability of AI to present complex materials with understandable language. Among the main disadvantages, the respondents noted unreliability of data and fake content. Despite the fact that students are generally positive about the use of AI, a significant part of respondents do not trust software products such as ChatGPT, since, in their opinion, it provides answers of average quality that need to be corrected. Based on the data obtained, the authors attempted to formulate recommendations on improving the methods of teaching and control in the process of teaching foreign languages at universities.

Unveiling Text Representation with 'Bag of Words'

Techniques for natural language processing (NLP) have grown to be essential tools for deciphering and drawing insightful conclusions from massive volumes of text data. A thorough review of numerous natural language processing (NLP) techniques, including as tokenization, stemming, lemmatization, named entity recognition, sentiment analysis, and topic modelling, is provided in this abstract. These methods are essential for applications like sentiment analysis, machine translation, text assistant categorization, and information retrieval. Furthermore, the capabilities of NLP systems have been greatly improved by recent developments in deep learning, especially with models like BERT and GPT. This has allowed them to reach state-of-the-art performance in a variety of language understanding tasks. The difficulties and potential paths for future study in NLP, including managing ambiguity, comprehending context, and enhancing multilingual assistance, are also highlighted in this abstract. Using NLP tools to their full potential, researchers.

Knowledge-based Data Processing for Multilingual Natural Language Analysis

Natural Language Processing (NLP) aids the empowerment of intelligent machines by enhancing human language understanding for linguistic-based human-computer communication. Recent developments in processing power, as well as the availability of large volumes of linguistic data, have enhanced the demand for data-driven methods for automatic semantic analysis. This paper proposes multilingual data processing using feature extraction with classification using deep learning architectures. Here, the input text data has been collected based on various languages and processed to remove missing values and null values. The processed data has been extracted using Histogram Equalization based Global Local Entropy (HEGLE) and classified using Kernel-based Radial basis Function (Ker_Rad_BF). These architectures could be utilized to process natural language. We present solutions to the multilingual sentiment analysis issue in this research article by implementing algorithms, and we compare precision factors to discover the optimum option for multilingual sentiment analysis. For the HASOC dataset, the proposed HEGLE_ Ker_Rad_BF achieved an accuracy of 98%, a precision of 97%, a recall of 90.5%, an f-1 score of 85%, RMSE of 55.6%, and a loss curve analysis attained 44%. For the TRAC dataset, the accuracy of 98%, the precision attained is 97%, the Recall is 91%, the F-1 score is 87%, and the RMSE of the proposed neural network is 55%.

Combining statistical dialog management and intent recognition for enhanced response selection

Abstract Conversational interfaces are becoming ubiquitous in an increasing number of application domains as Artificial Intelligence, Natural Language Processing and Machine Learning methods associated with the recognition, understanding and generation of natural language advance by leaps and bounds. However, designing the dialog model of these systems is still a very demanding task requiring a great deal of effort given the number of information sources to be considered related to the analysis of user utterances, interaction context, information repositories, etc. In this paper, we present a general framework for increasing the quality of the system responses by combining a statistical dialog management technique and a deep learning-based intention recognizer that allow replacing the system responses initially selected by the statistical dialog model with other presumably better candidates. This approach is portable to different task-oriented domains, a diversity of methodologies for dialog management and intention estimation techniques. We have evaluated our two-step proposal using two conversational systems, assessed several intention recognition methodologies and used the developed modules to dynamically select the system responses. The results of the evaluation show that the proposed framework achieves satisfactory results by making it possible to reduce the number of non-coherent dialog responses by replacing them by more coherent alternatives.

Serbian Version of the eHealth Literacy Questionnaire (eHLQ): Translation, Cultural Adaptation, and Validation Study Among Primary Health Care Users.

As digital health services are increasingly developing and becoming more interactive in Serbia, a comprehensive instrument for measuring eHealth literacy (EHL) is needed. This study aimed to translate, culturally adapt, and investigate the psychometric properties of the Serbian version of the eHealth Literacy Questionnaire (eHLQ); to evaluate EHL in the population of primary health care (PHC) users in Serbia; and to explore factors associated with their EHL. The validation study was conducted in 8 PHC centers in the territory of the Mačva district in Western Serbia. A stratified sampling method was used to obtain a representative sample. The Translation Integrity Procedure was followed to adapt the questionnaire to the Serbian language. The psychometric properties of the Serbian version of the eHLQ were analyzed through the examination of factorial structure, internal consistency, and test-retest reliability. Descriptive statistics were calculated to determine participant characteristics. Differences between groups were tested by the 2-tailed Students t test and ANOVA. Univariable and multivariable linear regression analyses were used to determine factors related to EHL. A total of 475 PHC users were enrolled. The mean age was 51.0 (SD 17.3; range 19-94) years, and most participants were female (328/475, 69.1%). Confirmatory factor analysis validated the 7-factor structure of the questionnaire. Values for incremental fit index (0.96) and comparative fit index (0.95) were above the cutoff of ≥0.95. The root mean square error of approximation value of 0.05 was below the suggested value of ≤0.06. Cronbach α of the entire scale was 0.95, indicating excellent scale reliability, with Cronbach α ranging from 0.81 to 0.90 for domains. The intraclass correlation coefficient ranged from 0.63 to 0.82, indicating moderate to good test-retest reliability. The highest EHL mean scores were obtained for the understanding of health concepts and language (mean 2.86, SD 0.32) and feel safe and in control (mean 2.89, SD 0.33) domains. Statistically significant differences (all P<.05) for all 7 eHLQ scores were observed for age, education, perceived material status, perceived health status, searching for health information on the internet, and occupation (except domain 4). In multivariable regression models, searching for health information on the internet and being aged younger than 65 years were associated with higher values of all domain scores except the domain feel safe and in control for variable age. This study demonstrates that the Serbian version of the eHLQ can be a useful tool in the measurement of EHL and in the planning of digital health interventions at the population and individual level due to its strong psychometric properties in the Serbian context.

Voice Assistant for Blind Person

This paper presents a comprehensive study on the design, implementation, and evaluation of a voice assistant system tailored specifically for visually impaired individuals. With the proliferation of voice-enabled devices, there exists a promising opportunity to empower visually impaired users through natural language interaction. Our proposed system incorporates advanced speech recognition, natural language understanding, and synthesis techniques to provide intuitive and efficient communication channels. Additionally, we address the unique challenges faced by visually impaired users, such as contextual understanding, accessibility, and usability. Through user studies and evaluations, we demonstrate the efficacy and usability of the proposed system, highlighting its potential to significantly enhance the independence and quality of life for visually impaired individuals in various daily tasks and activities

JEEMRC: Joint Event Detection and Extraction via an End-to-End Machine Reading Comprehension Model

Event extraction (EE) generally contains two subtasks: viz., event detection and argument extraction. Owing to the success of machine reading comprehension (MRC), some researchers formulate EE into MRC frameworks. However, existing MRC-based EE techniques are pipeline methods that suffer from error propagation. Moreover, the correlation between event types and argument roles is pre-defined by experts, which is time-consuming and inflexible. To avoid these issues, event detection and argument extraction are formalized as joint MRC. Different from previous methods, which just generate questions for argument roles for identified event types, questions are generated for all arguments that appear in the given sentence in our approach. Moreover, an end-to-end MRC model, JEEMRC, is proposed, which consists of an event classifier and a machine reader with a coarse-to-fine attention mechanism. Our proposed model can train two subtasks jointly to alleviate error propagation and utilizes interaction information between event types and argument roles to improve the performance of both tasks. Experiments on ACE 2005 verify that our JEEMRC achieves competitive results compared with previous work. In addition, it performs well when detecting events and extracting arguments in data-scarce scenarios.

WordCanvas: Text-to-Image Generation

This project investigates the novel use of stable dif- fusion techniques to generate high-quality images from detailed text descriptions. The combination of natural language under- standing and computer vision in text-to-image conversion opens up new possibilities for content creation and communication. Using cutting-edge stable diffusion models, our project builds a solid foundation for the generation process, which includes tokenization, pre-processing, specialized architecture design, and post-processing techniques. The advantages include eye-catching images, increased user engagement, content personalization, and improved accessibility. Automation of content generation has applications in marketing, education, data visualization, and creative expression. However, challenges such as model accuracy, ethical concerns, and biases need addressing. Achieving a balance between automation and human supervision is critical for the responsible application of this transformative capability. Index Terms—Stable diffusion, Text-to-image conversion, Nat- ural language understanding, Pre - processing, Post-processing techniques, Content personalization

Exploring noise-induced techniques to strengthen deep learning model resilience

AbstractIn artificial intelligence, combating overfitting and enhancing model generalization is crucial. This research explores innovative noise-induced regularization techniques, focusing on natural language processing tasks. Inspired by gradient noise and Dropout, this study investigates the interplay between controlled noise, model complexity, and overfitting prevention. Utilizing long short-term memory and bidirectional long short term memory architectures, this study examines the impact of noise-induced regularization on robustness to noisy input data. Through extensive experimentation, this study shows that introducing controlled noise improves model generalization, especially in language understanding. This contributes to the theoretical understanding of noise-induced regularization, advancing reliable and adaptable artificial intelligence systems for natural language processing.

YouTube Scholar: Advancing Education with AI Assistance

In the contemporary landscape of online educa- tion, the proliferation of educational content on platforms like YouTube has opened new avenues for learners. However, the sheer volume of videos presents a challenge, requiring innovative solutions. This project takes a pioneering approach by developing a specialized AI assistant for educational YouTube lectures, lever- aging advanced language models such as Generative Pretrained Transformers (GPT). This project focuses on the development of an AI assistant tailored specifically for educational YouTube lectures and courses. The primary goal is to enhance the learning experience by leveraging the capabilities of GPT-based models to provide personalised and contextually relevant interactions for learners. By harnessing the power of natural language understanding, generation, and dialog systems, the AI assistant aims to assist learners in navigating, understanding, and engaging with educa- tional content more effectively. The ultimate goal is to bridge the gap between the abundance of online educational content and learners’ comprehension, thereby revolutionizing the educational landscape in the digital age. By providing a sophisticated AI-driven companion, this project aspires to empower learners, making the educational journey more accessible, engaging, and conducive to deeper learning experiences. Through the integration of cutting-edge technologies, it envisions a future where learners can unlock the full potential of online education with confidence and ease

Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT

BackgroundPretraining large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirectional encoder representations from transformers (BERT), the performance of information extraction from free text has improved significantly in both the general and medical domains. However, it is difficult to train specific BERT models to perform well in domains for which few databases of a high quality and large size are publicly available. ObjectiveWe hypothesized that this problem could be addressed by oversampling a domain-specific corpus and using it for pretraining with a larger corpus in a balanced manner. In the present study, we verified our hypothesis by developing pretraining models using our method and evaluating their performance. MethodsOur proposed method was based on the simultaneous pretraining of models with knowledge from distinct domains after oversampling. We conducted three experiments in which we generated (1) English biomedical BERT from a small biomedical corpus, (2) Japanese medical BERT from a small medical corpus, and (3) enhanced biomedical BERT pretrained with complete PubMed abstracts in a balanced manner. We then compared their performance with those of conventional models. ResultsOur English BERT pretrained using both general and small medical domain corpora performed sufficiently well for practical use on the biomedical language understanding evaluation (BLUE) benchmark. Moreover, our proposed method was more effective than the conventional methods for each biomedical corpus of the same corpus size in the general domain. Our Japanese medical BERT outperformed the other BERT models built using a conventional method for almost all the medical tasks. The model demonstrated the same trend as that of the first experiment in English. Further, our enhanced biomedical BERT model, which was not pretrained on clinical notes, achieved superior clinical and biomedical scores on the BLUE benchmark with an increase of 0.3 points in the clinical score and 0.5 points in the biomedical score. These scores were above those of the models trained without our proposed method. ConclusionsWell-balanced pretraining using oversampling instances derived from a corpus appropriate for the target task allowed us to construct a high-performance BERT model.

Conversational Fashion Outfit Generator Powered by GenAI

The convergence of artificial intelligence and fashion has given rise to innovative solutions that cater to the ever-evolving needs and preferences of fashion enthusiasts. This report delves into the methodology behind the development of a "Conversational Fashion Outfit Generator powered by GenAI," an advanced application that leverages the capabilities of Generative Artificial Intelligence (GenAI) to create personalized fashion outfits through natural language interactions. The model outlines the essential elements of the methodology, including data collection, natural language understanding, computer vision integration, and deep learning algorithms. Data collection forms the bedrock, as access to a diverse dataset of fashion-related information is critical for training and fine-tuning AI models. Natural Language Understanding (NLU) is instrumental in comprehending user input and generating context-aware responses, ensuring meaningful and engaging conversations. Computer vision technology is integrated to analyze fashion images, recognizing clothing items, styles, and colors, thus aiding in outfit recommendations. Deep learning algorithms, particularly recurrent and transformer-based models, form the backbone of the system, generating personalized and contextually relevant fashion suggestions. This methodology not only underpins the "Conversational Fashion Outfit Generator" but also reflects the evolving landscape of AI in the fashion industry, where personalized, interactive experiences are becoming increasingly paramount in the realm of fashion and e-commerce.

Text Classification on Customer Feedback: A Systematic Literatures Review

User feedback in text classification serves multiple purposes, including refining models, enhancing datasets, adapting to user preferences, identifying emerging topics, and evaluating system performance, all of which contribute to the creation of more effective and user-centric classification systems. Many text classification techniques, including data mining, machine learning, and deep learning approaches, have been employed in previous literature, each making significant contributions to the field. This paper aims to contribute by guiding researchers seeking commonly used classification techniques and evaluation metrics in text processing. Additionally, it identifies the classification technique that generates higher accuracy and works as a basis for researchers to synthesize studies within their respective fields. PRISMA methodology is adapted to systematically review 28 current literatures on text classification on user feedback. The results obtained are guided by four research questions; paper distribution year, dataset source and size; evaluation metric and model accuracy. The review has shown that support vector machines (SVM) are frequently employed and consistently achieve high levels of accuracy as high as 97.17% with various datasets used. The future direction of this work could explore models that integrate sentiment analysis and natural language understanding to more accurately capture nuanced user opinions and preferences.

HCT: Chinese Medical Machine Reading Comprehension Question-Answering via Hierarchically Collaborative Transformer.

Chinese medical machine reading comprehension question-answering (cMed-MRCQA) is a critical component of the intelligence question-answering task, focusing on the Chinese medical domain question-answering task. Its purpose enable machines to analyze and understand the given text and question and then extract the accurate answer. To enhance cMed-MRCQA performance, it is essential to possess a profound comprehension and analysis of the context, deduce concealed information from the textual content and, subsequently, precisely determine the answer's span. The answer span has predominantly been defined by language items, with sentences employed in most instances. However, it is worth noting that sentences may not be properly split to varying degrees in various languages, making it challenging for the model to predict the answer zone. To alleviate this issue, this paper presents a novel architecture called HCT based on a Hierarchically Collaborative Transformer. Specifically, we presented a hierarchical collaborative method to locate the boundaries of sentence and answer spans separately. First, we designed a hierarchical encoding module to obtain the local semantic features of the corpus; second, we proposed a sentence-level self-attention module and a fused interaction-attention module to get the global information about the text. Finally, the model is trained by combining loss functions. Extensive experiments were conducted on the public dataset CMedMRC and the reconstruction dataset eMedicine to validate the effectiveness of the proposed method. Experimental results showed that the proposed method performed better than the state-of-the-art methods. Using the F1 metric, our model scored 90.4% on the CMedMRC and 73.2% on eMedicine.

Prevalence of Transitional Vertebra in Lumbar Disc Herniation in Bangabandhu Sheikh Mujib Medical University

Background: Lumbosacral transitional vertebrae (LSTV) occur as a congenital anomaly in the segmentation of lumbosacral spine that occur during intrauterine life. LSTV includes lumbarization and sacralization of the lumbosacral region. A transitional vertebra(TV) may have varying formations, the common feature being an atypical lumbosacral articulation between transverse process of the most inferior lumbar vertebra and the sacrum. There has been a lot of discussion regarding the prevalence of LSTV in lumbar disc herniation(LDH). Most of the studies showed increased prevalence while other studies showed TV is an incidental finding and there is no increased prevalence in LDH. LDH is a quiet common ailment encountered in neurosurgical practice. Numerous causes have been attributed to it. LSTV might have great importance in patients who clinically seem to have LDH where it’s presence in plain X-ray might provide a supportive evidence for diagnosis and it can help to counsel the patient. We will be able to recommend a plain x-ray of lumbosacral spine first, having history and clinical findings suggestive of LDH and can save the cost of MRI investigation until decision for operation. Beside this LSTV is an important entity for spinal surgeons , radiologists and also for those who do interventional procedures in the spine. For this reason the prevalence of TV in LDH should be known. This study was not carried out in our country yet. So this study will enrich our demographic information and will also help the spinal surgeons to counsel the patients about their congenital spinal morphological variation and different facts related with this. Objectives: General objective of this study is to observe the prevalence of LSTV in patients with LDH. Specific objective of this study was to identify the diathroidal joint or fusion between transverse process of last lumbar vertebra and ala of sacrum, to count the vertebral number from C2 to S1 in whole spine screening film, to identify the transitional vertebra as sacralization or lumbarization or absence of TV, to predict the future possibility of development of LDH from plain X-ray of lumbosacral spine. Methods: This study was a cross sectional type of observational study and was conducted in Department of Neurosurgery, Bangabandhu Sheikh Mujib Medical University (BSMMU). The patients (N=45) who fulfilled the selection criteria was enrolled in this study. Patient’s data were recorded in a predetermined data sheet. Patients were informed in details about the study, its merits and demerits in easy and understandable language and then informed consent was taken. Also assurance was given that all the information and records would be kept confidential and the study result would help the neurosurgeons to counsel the patients with LSTV about future possibility of development of LDH from plain X-ray of lumbosacral spine. This study was not responsible for any additional harm to the patient and study had no potential risk to the patient and no experimental drugs were used in this study. Results: A total of 45 patients of LDH were studied to see the prevalence of lumbosacral TV in LDH. Based on history and clinical findings, 100% of patients presented with low back pain, 100% of patients presented with sciatica, and 62.2% presented with gait difficulty and 02.2% of patients presented with cauda equina syndrome. Mean age of patients having LDH herniation was 38.08 ± 10.15 years. LSTV was found in 31.11% of patients with LDH. In this study, LSTV was diagnosed by plain X ray of Lumbosacral spine (A/P view) and supplemented with MRI of L/S spine with screening of whole spine (in most cases). Diagnosis of transitional vertebra was done by researcher and was further confirmed by guide and faculty members of BSMMU. In relation to gender, prevalence of LSTV in male was 25.9% and in female was 38.9% which indicated higher prevalence of TV in female but this is not statistically significant (p value 0.356).Incidence of disc herniation in patients with TV was at the space above the transition in 85.7% of cases. The most likely explanation for this is that the motion segment cephalad to the LSTV has to bear additional stresses by virtue of it being juxtaposed to a relatively non-mobile segment Conclusion: This study reveals that the prevalence of LSTV in LDH is 31.11% which is near the upper limit of its prevalence(according to literature). Association of TV with LDH can not be obtained as a control group of patient without LDH was not considered. So further study should be carried out incorporating large number of patients with control group with long study period to generalize the findings to target population. Bang. J Neurosurgery 2023; 12(2): 76-85

A Research Paper of a Medical Chatbot using Llama 2

Abstract: Embracing the knowledge era in healthcare, this research explores a novel medical chatbot empowered by the formidable Llama 2 language model. Transcending the limitations of conventional chatbots, it leverages a trifecta of technological prowess: Llama 2's vast knowledge base and nuanced understanding of medical language enable pinpointaccurate responses, even to intricate inquiries. Chainlit's web framework sculpts an intuitive interface, empowering users to effortlessly navigate and visualize pertinent medical information. Faiss, the master of information retrieval, swiftly delivers curated medical resources tailored to the user's specific needs This research paves the way for a future where medical chatbots, wielding the power of knowledge, empower individuals to navigate the complexities of healthcare with confidence.