Terms Of Frequency Research Articles

Reskilling and Upskilling the Workforce: Text Mining to Identify Patterns and Trends in Research

In the aftermath of the Fourth Industrial Revolution and the seismic shifts triggered by the COVID-19 pandemic, the labor market has undergone a significant transformation, compelling a reassessment of workforce competencies. This study delivers a comprehensive analysis of the emerging trends in reskilling and upskilling initiatives, which are pivotal for adapting to these shifts. Employing text mining techniques through Voyant Tools, we meticulously examined a corpus of 68 academic articles from the Web of Science database, all related to workforce development. Our methodological framework encompassed a thorough statistical analysis to distill and scrutinize term frequencies, inter-term relationships, and the overarching sentiment within the scholarly dialogue. The outcomes of this inquiry provide a sophisticated understanding of the prevailing academic terrain, accentuating the central themes and lacunae in the extant research corpus. The revelations from this preliminary study make a significant contribution to the domain of human capital development. The findings lay down an empirical groundwork for future studies in this sphere, poised to furnish strategic insights crucial for crafting robust career development programs. By illuminating the imperative of perpetual learning and skill enhancement, this paper adds to the conversation on equipping a resilient workforce, ready to excel in the dynamic, tech-centric economy that lies ahead.

Prevention of Anaemia in Pregnancy: A Five-Year Scoping Review

Abstract: Approximately 50% of pregnant women globally suffer from pregnancy-related anaemia, which is a serious issue for reproductive health and can lead to a number of complications, including low birth weights, premature deliveries, foetal deformities, and feto-maternal mortality. Aim: This study aimed to evaluate the suggested strategies to prevent anaemia in pregnancy through a 5-year scoping review (November, 2017 to November, 2022). Methods: A comprehensive list of keyword combinations linked to anemia prevention in pregnancy and pertinent approaches to inform the search strategy were evaluated. Significant events, including pregnancy and anemia, were cross-referenced with the frequency and variability of terms across time. Literature from Pubmed (MEDLINE), Google Scholar, PLOS One, and Cochrane database were reviewed. Results: Evidence-based preventative therapy options for iron deficiency anaemia in pregnancy include prophylactic iron supplementation, food fortification with iron and folate, and health education. There is a wealth of research on the effects of these therapies on the occurrence of anaemia during pregnancy. However, these prospects are endangered by several variables, such as inadequate health facilities, low maternal health literacy, low utilisation of maternal health care in developing countries, a dearth of resources for health education, and lack of a government policy that prevents anaemia during pregnancy. Conclusion: If the highlighted challenges are appropriately addressed through the provision of health education material, the effectiveness of anaemia prevention in pregnancy could be sustained.

Measurement of daily climate physical risks and climate transition risks faced by China's energy sector stocks

This paper introduces a novel approach for assessing the daily climate transition risk level in China and quantifying the climate physical risk associated with various fossil energy stocks. These measurements offer valuable data sources for empirical research in this field. In assessing climate transition risk, this paper employs the Term Frequency-Inverse Document Frequency (TF-IDF) method to construct a glossary of China's climate transition terminology. It also utilizes the multi-scale moment difference method to evaluate China's daily climate transition risk from 2000 to 2022. Climate physical risk measurement primarily focuses on boundary projection and base period shifting. The analysis reveals that: (1) China's climate policy documents emphasize areas such as energy conservation, meteorology, renewables, low-carbon solutions, and carbon emissions. Notably, meteorology-related provisions, including those for typhoons, rainfall, and drought, indicate heightened government attention to climate disaster response. (2) The research identifies periods of increased climate transition risk levels in 2004–2008 and 2017–2019, with decreases in 2011–2015 and 2020–2022. (3) The distribution of climate physical risk levels in China's fossil energy stocks is right-skewed, with the range [0.86, 0.88] representing the predominant distribution interval and concentration of these risks. The findings of this study can assist policymakers and investors in making informed decisions for market management and investments based on climate risk scenarios.

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients. We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment. The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user. Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings. EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.

Sentiment analysis of coronavirus data with ensemble and machine learning methods

The coronavirus pandemic has distanced people from social life and increased the use of social media. People's emotions can be determined with text data collected from social media applications. This is used in many fields, especially in commerce. This study aims to predict people's sentiments about the pandemic by applying sentiment analysis to Twitter tweets about the pandemic using single machine learning classifiers (Decision Tree-DT, K-Nearest Neighbor-KNN, Logistic Regression-LR, Naïve Bayes-NB, Random Forest-RF) and ensemble learning methods (Majority Voting (MV), Probabilistic Voting (PV), and Stacking (STCK)). After vectorizing the tweets using two predictive methods, Word2Vec (W2V) and Doc2Vec, and two traditional word representation methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BOW), classification models built using single machine learning classifiers were compared to models built using ensemble learning methods (MV, PV and STCK) by heterogeneously combining single machine classifier algorithms. Accuracy (ACC), F-measure (F), precision (P), and recall (R) were used as performance measures, with training/test separation rates of 70%-30% and 80%-20%, respectively. Among these models, the ACC of ensemble learning models ranged from 89% to 73%, while the ACC of single classifier models ranged from 60% to 80%. Among the ensemble learning methods, STCK with Doc2Vec text representation/embedding method gave the best ACC result of 89%. According to the experimental results, ensemble models built with heterogeneous machine learning classifier algorithms gave better results than single machine learning classifier algorithms.

Enhanced MNB Method for SPAM E-mail/SMS Text Detection Using TF-IDF Vectorizer

Spam, whether in the form of SMS or email, poses significant threats by compromising user privacy and potentially leading to unauthorized access to personal data. In the era of smartphones, where users store sensitive information, the risk of cyber-attack through spam messages is heightened. This research addresses the pressing issue of spam SMS and email detection using a dataset comprising 5574 messages from reputable sources. The collection includes contributions from the National University of Singapore SMS Corpus, Grumble text Website, Caroline Tag’s PhD Theses, and SMS Spam Corpus v.0.1 Big. With a meticulous approach encompassing data cleaning, balancing, preprocessing, and exploratory data analysis, the research employs the TF-IDF (Term Frequency and Inverse Document Frequency) vectorizer to enhance the model’s ability to capture the importance of individual words. This foundational work sets the stage for evaluating various machine learning models, including Support Vector Machine, Multinomial Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, AdaBoost, K-Nearest Neighbors, XGBoost, Gradient Boost Classifier, Bagging Classifier, and Extra Tree Classifier. Notably, the Multinomial Naïve Bayes model emerges as a standout performer with 100% accuracy and 97% precision in phishing detection. The research introduces an intuitive user interface, facilitating real-time interactivity for model assessment and offering valuable insights for cybersecurity applications. The study contributes to the advancement of robust cybersecurity systems, emphasizing precision and accuracy in spam SMS and email text detection.

FAKE NEWS DETECTION

In order to counteract the spread of false information, this study explores the use of machine learning techniques, particularly decision tree classifiers and TF-IDF (Term Frequency-Inverse Document Frequency). Using a dataset of tagged news items, the model is trained to identify patterns that differentiate reliable content from unreliable information.A survey's analysis reveals a worrying trend: fake news has been more common from 2017 to 2018, growing gradually before experiencing a noticeable spike in 2019. But applying current models in 2020 has resulted in a slowdown in the spread of false information.A well-liked machine learning approach called the decision tree classifier provides interpretability by showing decisions as a tree structure. This helps to clarify how the model determines which news is real and which is phony by identifying important characteristics that contribute to the categorization process.In contrast, TF-IDF plays a crucial role in natural language processing tasks by assigning weights to terms according to their significance within a corpus of documents. Through the integration of TF-IDF, the model is able to better identify misinformation by capturing the meaning of words in news stories.All things considered, this study offers a viable strategy to deal with the growing problem of false news by using machine learning techniques and conducting an empirical examination of its effects over time Keywords – Include at least 4 keywords or phrases, must be separated by commas to distinguish them.

Deciphering the Systemic Impact of Herbal Medicines on Allergic Rhinitis: A Network Pharmacological Approach

Allergic rhinitis (AR) is a systemic allergic disease that has a considerable impact on patients’ quality of life. Current treatments include antihistamines and nasal steroids; however, their long-term use often causes undesirable side effects. In this context, traditional Asian medicine (TAM), with its multi-compound, multi-target herbal medicines (medicinal plants), offers a promising alternative. However, the complexity of these multi-compound traits poses challenges in understanding the overall mechanisms and efficacy of herbal medicines. Here, we demonstrate the efficacy and underlying mechanisms of these multi-compound herbal medicines specifically used for AR at a systemic level. We utilized a modified term frequency–inverse document frequency method to select AR-specific herbs and constructed an herb–compound–target network using reliable databases and computational methods, such as the Quantitative Estimate of Drug-likeness for compound filtering, STITCH database for compound-target interaction prediction (with a high confidence score threshold of 0.7), and DisGeNET and CTD databases for disease-gene association analysis. Through this network, we conducted AR-related targets and pathway analyses, as well as clustering analysis based on target-level information of the herbs. Gene ontology enrichment analysis was conducted using a protein–protein interaction network. Our research identified 14 AR-specific herbs and analyzed whether AR-specific herbs are highly related to previously known AR-related genes and pathways. AR-specific herbs were found to target several genes related to inflammation and AR pathogenesis, such as PTGS2, HRH1, and TBXA2R. Pathway analysis revealed that AR-specific herbs were associated with multiple AR-related pathways, including cytokine signaling, immune response, and allergic inflammation. Additionally, clustering analysis based on target similarity identified three distinct subgroups of AR-specific herbs, corroborated by a protein–protein interaction network. Group 1 herbs were associated with the regulation of inflammatory responses to antigenic stimuli, while Group 2 herbs were related to the detection of chemical stimuli involved in the sensory perception of bitter taste. Group 3 herbs were distinctly associated with antigen processing and presentation and NIK/NF-kappa B signaling. This study decodes the principles of TAM herbal configurations for AR using a network pharmacological approach, providing a holistic understanding of drug effects beyond specific pathways.

An automated learning model for twitter sentiment analysis using Ranger AdaBelief optimizer based Bidirectional Long Short Term Memory

AbstractSentiment analysis is an automated approach which is utilized in process of analysing textual data to describe public opinion. The sentiment analysis has major role in creating impact in the day‐to‐day life of individuals. However, a precise interpretation of text still relies as a major concern in classifying sentiment. So, this research introduced Bidirectional Long Short Term Memory with Ranger AdaBelief Optimizer (Bi‐LSTM RAO) to classify sentiment of tweets. Initially, data is obtained from Twitter API, Sentiment 140 and Stanford Sentiment Treebank‐2 (SST‐2). The raw data is pre‐processed and it is subjected to feature extraction which is performed using Bag of Words (BoW) and Term Frequency‐Inverse Document Frequency (TF‐IDF). The feature selection is performed using Gazelle Optimization Algorithm (GOA) which removes the irrelevant or redundant features that maximized model performance and classification is performed using Bi LSTM–RAO. The RAO optimizes the loss function of Bi‐LSTM model that maximized accuracy. The classification accuracy of proposed method for Twitter API, Sentiment 140 and SST 2 dataset is obtained as 909.44%, 99.71% and 99.86%, respectively. These obtained results are comparably higher than ensemble framework, Robustly Optimized BERT and Gated Recurrent Unit (RoBERTa‐GRU), Logistic Regression‐Long Short Term Memory (LR‐LSTM), Convolutional Bi‐LSTM, Sentiment and Context Aware Attention‐based Hybrid Deep Neural Network (SCA‐HDNN) and Stochastic Gradient Descent optimization based Stochastic Gate Neural Network (SGD‐SGNN).

An ensemble model for predicting dispositions of emergency department patients

ObjectiveThe healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department (ED) overcrowding. An emerging solution lies in machine learning’s potential to predict ED dispositions, thus leading to promising substantial benefits. This study’s objective is to create a predictive model for ED patient dispositions by employing ensemble learning. It harnesses diverse data types, including structured and unstructured information gathered during ED visits to address the evolving needs of localized healthcare systems.MethodsIn this cross-sectional study, 80,073 ED patient records were amassed from a major southern Taiwan hospital in 2018–2019. An ensemble model incorporated structured (demographics, vital signs) and pre-processed unstructured data (chief complaints, preliminary diagnoses) using bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF). Two random forest base-learners for structured and unstructured data were employed and then complemented by a multi-layer perceptron meta-learner.ResultsThe ensemble model demonstrates strong predictive performance for ED dispositions, achieving an area under the receiver operating characteristic curve of 0.94. The models based on unstructured data encoded with BOW and TF-IDF yield similar performance results. Among the structured features, the top five most crucial factors are age, pulse rate, systolic blood pressure, temperature, and acuity level. In contrast, the top five most important unstructured features are pneumonia, fracture, failure, suspect, and sepsis.ConclusionsFindings indicate that utilizing ensemble learning with a blend of structured and unstructured data proves to be a predictive method for determining ED dispositions.

An efficient artificial intelligence approach for early detection of cross-site scripting attacks

Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim’s browser. The consequences of XSS attacks can be severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications.

Long term frequency stabilization and frequency drift suppression of the 313 nm laser

A 313 nm ultraviolet (UV) laser is stabilized through the Pound–Drever–Hall method, and its frequency drift is suppressed by a remote frequency drift correction system. The 313 nm laser is generated through an external cavity diode laser at 1252 nm and two cascaded second harmonic generation (SHG) stages. By locking the 626 nm from the first SHG stage to an ULE cavity and evaluating the beat signal between the 626 nm laser and an optical frequency comb, the 313 nm laser frequency instabilities of 1.80 ×10−12 at 1 s and 4.70 ×10−14 at 64 s averaging time are obtained. With the remote frequency drift correction system, the frequency drift of 313 nm laser is reduced to 0.02 (6) kHz/h. Individual ions in a beryllium Coulomb crystal cooled by the stable 313 nm laser are identified within the 240 s.

Sentiment analysis of movie reviews based on NB approaches using TF–IDF and count vectorizer

Sentiment analysis of movie reviews based on NB approaches using TF–IDF and count vectorizer

Machine Learning for Predictive Analysis of Otolaryngology Residency Letters of Recommendation.

Letters of recommendation (LORs) are a highly influential yet subjective and often enigmatic aspect of the residency application process. This study hypothesizes that LORs do contain valuable insights into applicants and can be used to predict outcomes. This pilot study utilizes natural language processing and machine learning (ML) models using LOR text to predict interview invitations for otolaryngology residency applicants. A total of 1642 LORs from the 2022-2023 application cycle were retrospectively retrieved from a single institution. LORs were preprocessed and vectorized using three different techniques to represent the text in a way that an ML model can understand written prose: CountVectorizer (CV), Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec (WV). Then, the LORs were trained and tested on five ML models: Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). Of the 337 applicants, 67 were interviewed and 270 were not interviewed. In total, 1642 LORs (26.7% interviewed) were analyzed. The two best-performing ML models in predicting interview invitations were the TF-IDF vectorized DT and CV vectorized DT models. This preliminary study revealed that ML models and vectorization combinations can provide better-than-chance predictions for interview invitations for otolaryngology residency applicants. The high-performing ML models were able to classify meaningful information from the LORs to predict applicant interview invitation. The potential of an automated process to help predict an applicant's likelihood of obtaining an interview invitation could be a valuable tool for training programs in the future. N/A Laryngoscope, 2024.

Parkinson's Disease Psychosis and the Marketing of Pimavanserin.

In 2016, Nuplazid (pimavanserin) became the first FDA-approved treatment for Parkinson's Disease Psychosis (PDP). We explored the possibility that PDP was a term created to market Nuplazid. We examined trends in perceptions of psychosis in Parkinson's disease from the 1990s to 2020 through MEDLINE search term frequency, neurology textbooks, guidance from professional societies, Acadia annual reports, sponsored websites, and a sponsored meeting held by the National Institutes of Health (NIH). We analyzed continuing medical education (CME) activities on PDP and analyzed the connection between payments by the manufacturer of pimavanserin and prescriptions. Our analysis of nine sponsored CME activities reveals misleading themes, including: PDP is common, progressive, and not always drug-induced; there is no such thing as a benign hallucination, and psychotic symptoms always worsen; PDP increases mortality; and competing treatments are ineffective or dangerous while pimavanserin is safe and effective for treating PDP. Industry-sponsored CME was used to disseminate inaccurate and misleading marketing messages on psychosis related to Parkinson's disease. Some professional societies and some textbooks also resisted the PDP label. Reframing PDP as a unique condition is a typical example of condition branding. The establishment of PDP expanded the use of pimavanserin and is likely to have resulted in many avoidable deaths.

Automated Marking System for Essay Questions

The stress of marking assessment scripts of many candidates often results in fatigue that could lead to low productivity and reduced consistency. In most cases, candidates use words, phrases and sentences that are synonyms or related in meaning to those stated in the marking scheme, however, examiners rely solely on the exact words specified in the marking scheme. This often leads to inconsistent grading and in most cases, candidates are disadvantaged. This study seeks to address these inconsistencies during assessment by evaluating the marked answer scripts and the marking scheme of Introduction to File Processing (CSC 221) from the Department of Computer Science, University of Uyo, Nigeria. These were collected and used with the Microsoft Research Paraphrase (MSRP) corpus. After preprocessing the datasets, they were subjected to Logistic Regression (LR), a machine learning technique where the semantic similarity of the answers of the candidates was measured in relation to the marking scheme of the examiner using the MSRP corpus model earlier trained on the Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. Results of the experiment show a strong correlation coefficient of 0.89 and a Mean Relative Error (MRE) of 0.59 compared with the scores awarded by the human marker (examiner). Analysis of the error indicates that block marks were assigned to answers in the marking scheme while the automated marking system breaks the block marks into chunks based on phrases both in the marking scheme and the candidates’ answers. It also shows that some semantically related words were ignored by the examiner.

Unveiling Gender Biases in Recruitment: A Natural Language Processing Approach

This paper investigates the potential of AI to identify gender biases in recruitment for senior management positions in businesses, dealing with many documents. It aims to unravel the impact of gender biases in job advertisements as a possible reason behind the underrepresentation of women in the corporate world. An innovative experiment that extracts and analyses 2.198 job offers published in February and September 2021 in the Financial Times newspaper is presented. Natural language techniques are used. These methods identify the most frequent terms and their appearance rate in the advertisements showing the gender biases they generate. By enabling the analysis of many documents, the method allows the accurate identification of gender biases. This use is unique in management studies. The results show a strong co-occurrence of terms associated with male roles in the studied sectors. The concept of agentic-communal role differentiation, rooted in the Identity, homosocial, and TM-TM theories, supports the findings. This knowledge will contribute to using NLP to discover gender biases in recruitment for high decision-making positions proposing action to improve the present situation. Should more women ascend to decision-making positions, selection processes should be improved to reflect a more neutral language. At the same time, cultural changes should be promoted in the corporate world toward more inclusive workplaces.

Development of an electronic dictionary of terminology for school textbooks

This research, funded under the BR11765535 “Development of Scientific and Linguistic Foundations and IT Resources to Expand the Functions and Improve the Culture of the Kazakh Language” project, aimed at advancing the scientific and linguistic foundations of the Kazakh language and enhancing its role in interethnic communication through digital resources. Focusing on the urgent need for specialized digital terminological dictionaries for school subjects, the project addresses the limitations of existing resources, particularly for school students. The article discusses the criteria for inclusion in the terminological dictionary, emphasizing the significance, frequency, and thematic relevance of terms across various subjects. The project has undertaken the compilation of a comprehensive database covering key school subjects in the Kazakh language.

Online Reviews Sentiment Analysis Based on High-Rise Residential Projects in Malaysia

The Malaysian property industry has suffered due to increasing numbers of unsold properties, especially high-rise residential types. Industry practitioners believed the absence of accurate market data for developers caused a mismatch where the developer failed to provide the right properties for the market demand. Thus, a crucial need to understand public opinion on sentiment and preferences for high-rise property. This study derived an online-reviews-based process for evaluating public sentiment and preferences to understand the reason behind the imbalances of supply and demand currently faced by the property industry in Malaysia. The present study introduced an alternative approach to the evaluation of public sentiments and preferences towards high-rise residential via sentiment analysis and interpreted the cause of positive and negative opinions via Term Frequency-Inverse Document Frequency (TF-IDF) analysis. To demonstrate the applicability of the proposed approach, an experiment based on multiple high-rise properties as a case study. The findings are expected to be employed by developers/government in the evaluation of their weaknesses and strengths on the high-rise properties project and how developers/government can improve certain areas.

Detecting Source Code Plagiarism in Student Assignment Submissions Using Clustering Techniques

In pragmatic courses, graduate students are required to submit programming assignments, which have been susceptible to various forms of plagiarism. Detecting counterfeited code in an academic setting is of paramount importance, given the prevalence of publications and papers. Plagiarism, defined as the unauthorized replication of written work without proper acknowledgment, has become a critical concern with the advent of information and communication technology (ICT) and the widespread availability of scholarly publications online. However, the extensive use of freeware text editors has posed challenges in detecting source code plagiarism. Numerous studies have investigated algorithms for revealing different types of plagiarism and detecting source code plagiarism. In this research, we propose an innovative strategy that combines TF-IDF (Term Frequency-Inverse Document Frequency) modifications with K-means clustering, achieving a remarkable precision rate of 99.2%. Additionally, we explore the hierarchical clustering method, which estimates an even higher precision rate of 99.5% compared to previous techniques. To implement our approach, we utilize the Python programming language along with relevant libraries, providing a robust and efficient system for source code plagiarism detection in student assignment submissions.