NATURAL LANGUAGE PROCESSING IN ARTIFICIAL INTELLIGENCE: ENHANCING COMMUNICATION AND UNDERSTANDING

NPL is one of the AI models that have changed the course of AI by including natural language processing abilities. Thus, the study has discussed the possibilities of NPL in order to enhance communication and understanding. In addition, related questions and objectives were discussed based on the topic in the introduction part. Literature Review- Here, clear challenges and possibilities of natural language processing have been provided through analyses of previous works and critical debates in the literature review. Additionally, studies have developed fresh viewpoints based on literary data. Methodology- The paper includes a theoretical debate based on “The Situated Theory of Language”. Additionally, a thematic analysis based on the elements that influenced the research's development was offered. Findings and Analysis- It was noted that the implication of sentimental analysis and consumer perspective was found to be beneficial for the NPL implication at a mass level. Discussion- The study's discussion section covered each of the study's results in detail and also included a separate discussion of the findings. Conclusion- The analytical section concludes the overall analysis. A summary of the study's overall results is also provide

SENTIMENT ANALYSIS OF COMMENTS ON GOOGLE PLAY STORE, TWITTER AND YOUTUBE TO THE MYPERTAMINA APPLICATION WITH SUPPORT VECTOR MACHINE

Application is an important requirement in a business because it makes work more efficient thereby increasing the results of the company, pertamina as a supplier of fuel oil (BBM) in Indonesia provides the latest innovations by launching the mypertamina application for purchasing BBM which raises public opinion, and conveys its aspirations in social media. Text mining is a way to group community comments because text mining has an analysis that focuses on analyzing a comment that is extracted into information. The purpose of this study was to determine public sentiment towards the use of mypertamina by classifying comments using the Support Vector Machine (SVM) algorithm and finding the best kernel among linear, polynomial and RBF. In this study, data was taken from three social media, namely Google Play Store with 18.000 data, Twitter with 20.000 data and YouTube with 6.400 data with a total of 44.400 data. Sentiment is carried out by giving positive and negative classes, the accuracy obtained from sentiment is carried out for Google Play Store data of 95%, Twitter 76% and YouTube 99% and it is known that the best svm kernel in this study is the RBF kernel which outperforms the linear and polynomial kernels

A hybrid deep learning and NLP based system to predict the spread of Covid-19 and unexpected side effects on people

The aim of this paper is to deeply analyze the unexpected side effects of people during the Covid-19 pandemic using the RNN based NLP sentiment analysis model. The normalized correlation values that is obtained by computing the cases values between the people behavior extracted and covid-19 reported case also has values close to 1 million by the end of June 2020 provided in dataset. In this research work, with more time, we would like to continue from the results we have achieved by training the RNN with NLP based sentiment analysis model for more extended periods of time for predicting the behavior of people during Covid-19 pandemic with 76.71% of accuracy which is high as compared to the CNN, such as days or weeks, in order to see how results can improve. The advancement in this field created an urge in me to research more on the techniques and methodologies developed for covid-19 extraction. During the outbreak of an epidemic, it is of immense interest to monitor the effects of containment measures and forecast of outbreak including epidemic peak affecting the behavior of people. To confront the change in behavior, a simple RNN based NLP sentiment analysis model is used to simulate the number of affected patients of Coronavirus disease. Our initial problem formulation involved investigating the ideal conditions and preprocessing for working with a specific NLP task: predicting the behavior during the specific time of May 20 – June 20 in 2020 for all four traits of common people during the Covid-19 pandemic

Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions

Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education. This study evaluated the capabilities of leading LLMs, including GPT-4, GPT-3.5, PaLM2, Claude2, and SenseNova, in answering conceptual biology questions. The models were tested on a 108-question multiple-choice exam covering biology topics in molecular biology, biological techniques, metabolic engineering, and synthetic biology. Among the models, GPT-4 achieved the highest average score of 90 and demonstrated the greatest consistency across trials with different prompts. The results indicated GPT-4's proficiency in logical reasoning and its potential to aid biology research through capabilities like data analysis, hypothesis generation, and knowledge integration. However, further development and validation are still required before the promise of LLMs in accelerating biological discovery can be realized

USE OF GAMIFICATION MODEL FOR HOMEROOM TEACHERS IN CONDUCTING LEARNING ASSESSMENT (Qualitative Study)

Gamification is a game element but is included in non-game contexts because it is an effective motivational tool. The performance performed by using gamification becomes more attractive to users. The focus of the problem is determined by the researcher based on the field of education, with the object of research being a homeroom teacher. Education standards are at the level all stakeholders require, especially learning assessment. The urgency of students who are entitled to an evaluation from the teacher and easily known by the school and forwarded to parents. The approach taken with a qualitative study provides a more detailed explanation to homeroom teachers about problem solutions to place gamification in student learning assessments. Data were collected based on experiments with several homeroom teachers feeling and exploring media. The methods used in the qualitative study were interviews, observation and document analysis. Findings of homeroom teacher responses who were very satisfied with gamification. Homeroom teachers who received encouragement in entering student grades in a fun way. From the results of the study, it can be concluded that the implementation of gamification strategies where homeroom teachers conduct learning assessment triggers interest in fun characters, overcomes boredom with challenges and competitions, gains new knowledge, and improves thinking skills to strengthen social interaction

Text Data Mining for Uncovering the Influence of Religion on Ancient Greek Philosophical Thought with Optimization

Text data mining can provide valuable insights into the influence of religion on the development of ancient Greek philosophical thought. This paper presented text data mining techniques to perform feature extraction and classification with Gaussian Optimization (FeCGO), to analyze the influence of religion on the development of ancient Greek philosophical thought. This paper explores the application of text data mining techniques, specifically feature extraction and classification with Gaussian Optimization (FeCGO), to analyze the influence of religion on the development of ancient Greek philosophical thought. The FeCGO examined the relevant texts, including works by ancient Greek philosophers, religious texts, myths, and historical accounts. These texts are subjected to preprocessing steps, such as tokenization, stop word removal, stemming, and normalization, to ensure the data is prepared for analysis. The proposed FeCGO method combines the Gaussian Optimization algorithm with a classification model to optimize the classification accuracy and performance. Labeled data is used to train the FeCGO model, with texts categorized based on their religious or philosophical themes. The findings contribute to a deeper understanding of the interplay between religion and philosophy in ancient Greek society. The application of text data mining techniques, specifically FeCGO, demonstrates the potential of computational methods to extract valuable insights from large-scale textual datasets

Semantic-Driven 3D Scene Construction Based on Spatial Relationship and Case-Base

At present, 3D scene construction technology has been widely used in various fields. However, 3D scene construction is increasing on the human and material costs, and the production process is also complicated. First, we use a semantic analysis method to achieve better Chinese automatic word segmentation, which is bidirectional matching Chinese word segmentation based on N-gram model. Next, to solve the problems of low degree of automation and intelligence, we propose a new method of 3D scene construction based on spatial relationship and case-base. The objects and spatial relationships extracted from the scene description texts form a spatial constraint in the form of a triple, which is stored in the spatial relationship template library. Then the 3D model in the case-base is invoked to build the scene. This method takes the spatial constraint as the smallest module of the scene construction, which not only accelerates 3D scene construction, but also improves the rationality of the scene layout. At last, we apply this method to scene generation in a strategy game, in which the effectiveness and efficiency of the new method are proved

DrugExBERT for Pharmacovigilance – A Novel Approach for Detecting Drug Experiences from User-Generated Content

Pharmaceutical companies have to maintain drug safety through pharmacovigilance systems by monitoring various sources of information about adverse drug experiences. Recently, user-generated content (UGC) has emerged as a valuable source of real-world drug experiences, posing new challenges due to its high volume and variety. We present DrugExBERT, a novel approach to extract adverse drug experiences (adverse reaction, lack of effect) and supportive drug experiences (effectiveness, intervention, indication, and off-label use) from UGC. To be able to verify the extracted drug experiences, DrugExBERT additionally provides explications in the form of UGC phrases that were critical for the extraction. In our evaluation, we demonstrate that DrugExBERT outperforms state-of-the-art pharmacovigilance approaches as well as ChatGPT on several performance measures and that DrugExBERT is data- and drug-agnostic. Thus, our novel approach can help pharmaceutical companies meet their legal obligations and ethical responsibility while ensuring patient safety and monitoring drug effectiveness

A Systematic Literature Review of the Use of Computational Text Analysis Methods in Intimate Partner Violence Research

Purpose: Computational text mining methods are proposed as a useful methodological innovation in Intimate Partner Violence (IPV) research. Text mining can offer researchers access to existing or new datasets, sourced from social media or from IPV-related organisations, that would be too large to analyse manually. This article aims to give an overview of current work applying text mining methodologies in the study of IPV, as a starting point for researchers wanting to use such methods in their own work. Methods This article reports the results of a systematic review of academic research using computational text mining to research IPV. A review protocol was developed according to PRISMA guidelines, and a literature search of 8 databases was conducted, identifying 22 unique studies that were included in the review. Results: The included studies cover a wide range of methodologies and outcomes. Supervised and unsupervised approaches are represented, including rule-based classification (n = 3), traditional Machine Learning (n = 8), Deep Learning (n = 6) and topic modelling (n = 4) methods. Datasets are mostly sourced from social media (n = 15), with other data being sourced from police forces (n = 3), health or social care providers (n = 3), or litigation texts (n = 1). Evaluation methods mostly used a held-out, labelled test set, or k-fold Cross Validation, with Accuracy and F1 metrics reported. Only a few studies commented on the ethics of computational IPV research. Conclusions: Text mining methodologies offer promising data collection and analysis techniques for IPV research. Future work in this space must consider ethical implications of computational approaches

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

[EN] Background: Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people & rsquo;s health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. Objective: To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. Methods: A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. Results: The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N=98) followed by Health Emergencies (N=16) and Better Health and Wellbeing (N=15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7%, N=28). The reviews featured analytics primarily over both public and private data sources (67.44%, N=87). The most used type of data was medical imaging (31.8%, N=41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4%, N=56), in which Support Vector Machine method was predominant (20.9%, N=27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4%, N=47). (...)Martinez-Millana, A.; Saez-Saez, A.; Tornero-Costa, R.; Azzopardi-Muscat, N.; Traver Salcedo, V.; Novillo-Ortiz, D. (2022). Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. International Journal of Medical Informatics. 166:1-12. https://doi.org/10.1016/j.ijmedinf.2022.10485511216