Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g. physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, it describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation at health systems and in industry.

As RPG has high sales and profits, lots of developers have supplied various RPG to market but it changed to mass production type with sensational advertising, low quality and excessive charging and similar contents which affects game market and users’ game play experience. The author of this paper studied ways to improve mobile RPG by collecting and analyzing users’ reviews using crawling on Google Play Store. The author of this paper used topic modeling that uses text mining technique and LDA (Latent Dirichlet Allocation) to extract meaningful information from collected big data and visualized it. Inferring users’ reviews, figuring out opinions objectively and seeking ways to improve games are helpful in improving mobile RPG that can be played continuously.

In software development, developers received bug reports that describe the software bug. Developers find the cause of bug through reviewing the code and reproducing the abnormal behavior that can be considered as tedious and time-consuming processes. The developers need an automated system that incorporates large domain knowledge and recommends a solution for those bugs to ease on developers rather than spending more manual efforts to fixing the bugs or waiting on Q&A websites for other users to reply to them. Stack Overflow is a popular question-answer site that is focusing on programming issues, thus we can benefit knowledge available in this rich platform. This paper, presents a survey covering the methods in the field of mining software repositories. We propose an architecture to build a recommender System using the learning to rank approach. Deep learning is used to construct a model that solve the problem of learning to rank using stack overflow data. Text mining techniques were invested to extract, evaluate and recommend the answers that have the best relevance with the solution of this bug report.

Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textural document may reveal some additive information regarding the document that is not reflected in any other features from traditional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textural documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification.

At present, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed and treatment results. EMR has been recognized as a valuable resource for large scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation and data reduction. For semi-structured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (Named Entity Recognition) and RE (Relation Extraction). In this paper, we introduce the process of EMR processing, including data collection, data preprocessing, data mining, evaluation and knowledge application, analyze the current status of the key technologies, such as data preprocessing and data mining, and provide an overview of the application domains and prospects of EMR mining technologies. Finally, we summarize the existing problems in the research of EMR mining, and review the development trends.

For highly time-constrained very short-term investors, reading and extracting valuable information from financial news poses significant challenges. The wide range of topics covered in these news articles further compounds the difficulties for investors. The diverse content adds complexity and uncertainty to the text, making it arduous for very short-term investors to swiftly and accurately extract valuable insights. Variations in authors, media sources, and cultural backgrounds also introduce additional complexities. Hence, performing a bull-bear semantic analysis of financial news using text mining technologies can alleviate the volume, time, and energy pressures on very short-term investors while enhancing the efficiency and accuracy of their investment decisions. This study proposes labeling bull-bear words from a futures corpus detection method that extracts valuable information from financial news, allowing investors to understand market trends quickly. Generative AI models are trained to provide real-time bull-bear advice, aiding investors in adapting to market changes and devising effective trading strategies. Experimental results show the effectiveness of various models, with Random Forest and SVMs achieving an impressive 80% accuracy rate. MLP and Deep learning models also perform well. By leveraging these models, the study reduces the time spent reading financial articles, enabling faster decision-making and increasing the likelihood of investment success. Future research can explore the application of this method in other domains and enhance model design for improved predictive capabilities and practicality.

Subway construction is often in a complex natural and human-machine operating environment, and that complicated setting leads to subway construction more prone to safety accidents, which can cause substantial casualties and monetary losses. Thus, it is necessary to investigate the safety risks of subway construction. The existing literature on the identification and assessment of subway construction safety risks(SCSR) is susceptible to the influence of subjective factors. Moreover, although existing studies have explored the interrelationships between different risks, these studies usually analyze the interrelationships of single risks, lack the study of risk chain transfer relationships, and fail to find out the key path of risk transfer. Therefore, this paper innovatively combines text mining, association rules and complex networks to deep mine subway construction safety incident reports and explore risk transfer process. Firstly, it uses text mining technology to identify subway construction safety risk; Then, association rules are introduced to explore the causal relationships among safety risk; Finally, the key safety risk and important transfer paths of subway construction safety accidents (SCSA) are obtained based on the complex network model. Research results show that (a) improper safety management, unimplemented safety subject responsibilities, violation of operation rules, non-perfect safety responsibilities system and insufficient safety education and training are the key safety risk in SCSA; (b) two shorter key risk transfer paths in the subway construction safety network can be obtained: insufficient safety education and training→lower safety awareness→violation of operation rules→safety accidents; insufficient safety checks or hidden trouble investigations→violation of operation rules→safety accidents; (c) in the process of risk transfer, the risk can be controlled by controlling the key nodes or cutting off the transfer path. The results of the study provide new ideas and methods for SCSR identification and influence element mining, which help safety managers propose accurate subway construction safety risk control measures.

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, in particular to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel Smell Tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with emotional charge of situations. The experimental results show that we can detect smells and emotions with F1 score of 92.7 and 79.2, respectively.

(1) Background: The CEO message of hospital homepage contain various contents such as the hospital's future vision, promises with customers, upgraded services and public activities. The CEO’s message of the homepage includes non-financial information as well as financial information of corporates. Also, it provides useful information for not only company's goals and vision but also firm performance and strategies for the future. This study aims to investigate associations between CEO’s message of hospitals homepages and financial status. We used the balanced scorecard frame to analyze what content on the hospital's homepage is related to the hospital's various financial ratios. (2) Methods: We adopt a text mining method to extract significantly repeated keywords from the CEO’s message of hospital website. And we classify these keywords by a balanced scorecard frame. To examine the relationship between keywords of CEO’s message of the hospital homepage and hospital’s financial ratio, T-test is conducted for the difference in the TF-IDF (Term Frequency is Divided by Inverse Document Frequency) mean of the home page contents and its relationship with the views of the balanced scorecard framework. (3) Results: According to empirical results on 65 samples collected from local hospitals, there are some significant relationship between the qualitative content of the hospital's homepage and the quantitative financial ratio that indicates profitability, activity, leverage, liquidity, and transfer to essential business fund (EBF) income. (4) Conclusions: The introduction section of a homepage is most accessible to customers, containing the aims and ideals of hospitals and reflecting their values and visions [1]. In addition, in view of financial status, they can either emphasize financial strength or focus on other areas to mask weakness of financial information. This study reminds us of the importance of hospital website’s disclosure, and it can be inferred from the financial status of the hospital. It also highlights the need for harmonization between quantitative data, financial statements, and qualitative data, CEO’s messages. (5) Implications: To our best knowledge, this paper is the first research attempting to investigate the relation between text of hospital homepage and financial ratio of hospital through text mining technique and balanced scorecard frame. Hospitals take a crucial part in a country’s welfare and healthcare backbone industry. Nevertheless, in many countries, hospital organization sectors tend to remain a source of critical fiscal deficits due to its ineffective and sloppy management. We expect that the result of this paper can provide hospital managers to useful information.

This text mining study delves into the multifaceted contributions of the energy sector to the Sus-tainable Development Goals (SDGs). By analyzing a wide range of academic literature, we un-cover key themes, trends, and challenges shaping the intersection of energy and sustainability. The findings reveal that the energy sector plays a pivotal role in achieving SDGs such as affordable and clean energy (SDG 7) and climate action (SDG 13). Critical issues encompass governance, policy frameworks, and technological innovations. This research underscores the need for inter-disciplinary collaboration and holistic approaches in addressing complex energy-related sus-tainability challenges. The insights derived here provide guidance to policymakers, researchers, and stakeholders seeking to harness the energy sector's potential for a more sustainable and eq-uitable future.

The emergence of the Covid-19 pandemic has created both negative and positive changes, including implementing the circular economy across the globe. This Systematic Review follows the PRISMA statement and employs the Text Mining (Voyant Tools) technique to visualize and analyze the impacts of the Covid-19 on three aspects of the circular economy: economic, social, and environmental. The research employs Latent Dirichlet Allocation (LDA) to identify five major topics: (1) Shortage of medical equipment but high medical waste during Covid-19 due to the high demand in healthcare; (2) The long term negative impacts of lockdown on economic and social activities because of Covid-19 pandemic; (3) The reports on impacts of Covid-19 pandemic on the manufacturing globally, and their coping strategies and new opportunities; (4) The impacts of international restriction on the tourism, trade, shipping, and aviation industry, causing billion-dollar losses; (5) The reduction of pollution with health environment improvements with example cases from China and EU. The research identifies current literature gaps in the circular economy and Covid- 19 topics and encourages the application of text mining tools into researching to stimulate the research process and assist in communicating with the public.

The large quantity of information retrieved from communities, public data repositories, web pages, or data mining can be sparsed and poorly classified. This work shows how to employ unsupervised classification algorithms such as K-means proper to classify user reviews into their closest category, forming a balanced data set. Moreover, we found that the text vectorization technique significantly impacts the clustering formation, comparing TF-IDF and Word2Vec. The value for mapping a cluster with movie genre was 81.34% ± 20.48 of the cases when the TF-IDF was applied, whereas Word2Vec only yielded a 53.51% ± 24.1. In addition, we highlight the impact of the removal of stop-words. Thus, we detected that pre-compiled lists are not the best method to remove stop-words before clustering because there is much ambiguity, centroids are poorly separated, and only 57% of clusters could match a movie genre. Thus, our proposed approach achieved a 94% of accuracy. After analyzing the classifiers’ results, we appreciated a similar effect when divided by the stop-words method removal. Statistically significant changes were observed, especially in precision metric and Jaccard scores in both classifiers, using custom-generated stop lists rather than pre-compiled ones. Reclassifying sparse data is strongly recommended as using custom-generated stop lists.

Plagiarism means taking another person’s work and not giving any credit to them for it. Plagiarism is one of the most serious problems in academia and among researchers. Even though there are multiple tools available to detect plagiarism in a document but most of them are domain-specific and designed to work in English texts, but plagiarism is not limited to a single language only. Bengali is the most widely spoken language of Bangladesh and the second most spoken language in India with 300 million native speakers and 37 million second-language speakers. Plagiarism detection requires a large corpus for comparison. Bengali Literature has a history of 1300 years. Hence most Bengali Literature books are not yet digitalized properly. As there was no such corpus present for our purpose so we have collected Bengali Literature books from the National Digital Library of India and with a comprehensive methodology extracted texts from it and constructed our corpus. Our experimental results find out average accuracy between 72.10 % - 79.89 % in text extraction using OCR. Levenshtein Distance algorithm is used for determining Plagiarism. We have built a web application for end-user and successfully tested it for Plagiarism detection in Bengali texts. In future, we aim to construct a corpus with more books for more accurate detection.

The digital breakthrough of the nineties has profoundly changed the way events are recognized. Starting from EST (Event Segmentation Theory) elaborated by J.M. Zacks and his collaborators, this contribution shows how the way we segment the experiential flow has been changed by the introduction of digital: while throughout modernity the perception of events was based on immediate segmentation processes, founded on five elements (agent, space, time, intention, purpose) and determined by an evident con-clusion, we currently see in the perceptive models of individuals and consequently also in aesthetic texts (films, television series, novels, graphic novels etc.) a weakening of the end. This determines not only a greater difficulty in perceiving an event, but also the need to introduce micro-caesuras between one event and another: it is space that con-stitutes the most salient category in differentiating one event from another, as is evident in the transmedia storytelling that characterizes contemporary aesthetics.

Antimicrobial resistance (AMR) is one of the top 10 threats affecting global health. AMR defeats the effective prevention and treatment of infections caused by microbial pathogens including bacteria, parasites, viruses and fungi (WHO). Microbial pathogens have natural tendency to evolve and mutate over time resulting in AMR strains. The set of genes involved in antibiotic resistance also termed as “antibiotic resistance genes” (ARGs) spread through species by lateral gene transfer thereby causing global dissemination. While this biological mechanism is prevalent in the spread of AMR, human methods also augment through various mechanisms such as over prescription, incomplete treatment, environmental waste etc. A considerable portion of scientific community is engrossed in AMR related work trying to discover novel therapeutic solutions for tackling resistant pathogens. Comprehensive inspection of the literature shows that diverse therapeutic strategies have evolved over recent years. Collectively, these therapeutic strategies include novel small molecules, newly identified antimicrobial peptides, bacteriophages, phytochemicals, nanocomposites, novel phototherapy against bacteria, fungi and virus. In this work we have developed a comprehensive knowledgebase by collecting alternative antimicrobial therapeutic strategies from literature data. We have used subjective approach for datamining new strategies resulting in broad coverage of entities and subsequently add objective data like entity name, potency, safety information etc. The extracted data was organized KOMBAT (Knowledgebase Of Microbes’ Battling Agents for Therapeutics). A lot of these data are tested against AMR pathogens. We envision that this database will be noteworthy for developing future therapeutics against resistant pathogens. The database can be accessed through http://kombat.igib.res.in/.

The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.

Bio-molecular reagents like antibodies required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Bio-Resource Exchange that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, in order to index donations outside of our portal, we mined scientific articles to find instances of donations of antibodies and attempted to extract information about these donations at the finest granularity. Specifically, we extracted the name of the donor, his/her affiliation and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we propose two approaches – a rule based algorithm and a bootstrapped relation learning algorithm. The algorithms extracted donor names, affiliations and antibody names with average accuracies of 57% and 62% respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future. Contact: madhavi@pitt.edu, madhavi@cs.cmu.edu Database URL: http://tonks.dbmi.pitt.edu/brx Supplementary information: Supplementary data are available at Database online.

This study combines insights from psycholinguistics and text analysis to identify linguistic markers of intercultural competence (ICC) in 1,635 blogs about intercultural experiences, written by 672 Hotel Management students. By combining holistic ICC frameworks with a text-analytical approach at word level, we were able to demonstrate that blogs with a high perceived level of ICC contain significantly more I-words, more insights words and less quantifiers. These markers of ICC constitute concrete cues for teachers when assessing reflective writing assignments and allow them to pinpoint concrete areas for improvement in their feedback and interaction with students.

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents is growing enormously in recent years through instant messaging, social networking posts, blogs, online portals, and other digital platforms. Unfortunately, the misapplication of technologies has boosted with this rapid growth of online content which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier `tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

American Sign Language is a popular language for deaf individuals. Communication is made easier for these people through sign language. However, in a digital era like today, there is a need for these people to be able to communicate online, and even get help from technology to communicate in person with non sign language speakers. This research will present a program able to translate American sign language to plain English. This study aims to use the OpenCV library to recognize hand signals, also a trained model to identify images so that the program can then translate them to words and letters. The program uses a data set of over 2000 images which will be in this case the largest data set available. With over 90\% of accuracy it results in a basic computer program with the largest data set available that would make possible for users to communicate with a wide variety of words and expressions.

This paper compares the performance of three text-to-speech (TTS) models released from June 2021 to January 2022 in order to establish a baseline for Brazilian Portuguese. Those models were trained using dataset for Brazilian Portuguese. The experimental setup considers tts-portuguese dataset to fine-tune the following TTS models: VITS end-to-end model; glowtts and gradtts acoustic models both using hifi-gan vocoder. Performance metrics are arranged into objective and subjective metrics. As subjective metrics, the naturalness and intelligibility are measured based on the mean opinion score (MOS). Results shows that gradtts+hifigan model achieved naturalness of 4.07 MOS, close to performance of current commercial models.

Simple measures often couldn’t count a deep complexity. In the case of semantic complexity, conventional readability formulas share a common style, a common sort of achievements and a common borders of limitation: These formulas lack a semantics-aware approach and as a result, a precise measurement of semantic complexity couldn’t be done. In this paper, we introduce DASTEX, a novel semantics-aware complexity measure for semantic complexity of text. By DASTEX, a new layer of complexity analysis are opened for NLP, cognitive and computational tasks. This measure benefits from an intuitionistic underlying formal model which consider semantic as a lattice of intuitions. This yields to a well-defined definition for semantic of a text and its complexity. DASTEX is a practical analysis method upon this formal model. So a complete suite of idea, model and method are prepared to result in a simple but yet deep measure for semantic complexity of text. The evaluation of the proposed approach is done by 4 Experiments. The results show DASTEX is capable of measuring the semantic complexity of text in 6 application-tasks.

Electronic invoicing has become mandatory for Italian companies since January 2019. Invoices are structured in a predefined xml template where the information reported can be easily extracted and analyzed. The main aim of this paper is to exploit the information structured in electronic invoices to build an intelligent system which can facilitate accountants work. More precisely, this contribution shows how it is possible to automate part of the accounting process: all sent or received invoices of a company are classified into specific codes which represent the economic nature of the the financial transactions. In order to classify data contained in the invoices a machine learning multiclass classification problem is proposed using as input variables the information of the invoices to predict two different target variables, account codes and the VAT codes, which composes a general ledger entry. Different approaches are compared in terms of prediction accuracy. The best performance is achieved considering the hierarchical structure of the account codes.

Ontology is a formalized representation of the problem area (PrA). Representation of the PrA in the form of an domain ontology is often used in the process of development of intelligent software systems and used as a knowledge base. The process of building an ontology is complex and requires an expert in the PrA. A large number of researchers are working to solve this problem. The basis of our approach is the use of a pipeline of different linguistic methods of text analysis. The set of rules developed by us is used to build an ontology based on the content analysis of a text resource. This article describes the method of building a domain ontology based on the linguistic analysis of content of text resources, presents an example of the proposed approach, and also presents the architecture of our pipeline.

With the abundance of conversations happening everywhere, dialogue summarization plays an increasingly important role in the real world. However, dialogues inevitably involve lots of personal pronouns, which hinder the performance of existing dialogue summarization models. This work proposes a framework named WHORU to inject external personal pronoun resolution (PPR) information into abstractive dialogue summarization models. To reduce time and space consumption, we further propose a simple and effective PPR method for the dialogue domain. Experiments demonstrated the superiority of the proposed methods. More importantly, we achieve new SOTA results on SAMSum and AMI datasets.

This research used an experimental study design in which 60 subjects were randomly assigned to either an experimental (image e-book guidance) group or a control (text guidance) group after meeting the admission conditions and agreeing to participate in the study. It was proven that providing image e-book guidance before surgery significantly changed the behavior of patients and promoted relief of urinary catheter discomfort through self-efficacy to reduce urinary catheter pain after surgery. It was found that providing image e-book guidance can shorten the time for health education and provide patients with repeated medical education and familiarity with health guidance, which can help to address the important clinical service demand issue and the shortage of nursing staff.

Recent advances in deep learning technology have led to increased interest in text-to-image technology, which enables computers to create images from text by simulating the human process of forming mental images. The GAN-based text-to-image technology involves the extraction of features from input text, which are combined with noise and then used as input to a GAN that generates images that are similar to the original images through competition between the generator and discriminator. Although generating images from English text is a mature area of research, text-to-image technology based on multilingualism, such as Korean, is still in its early stages of development. Webtoon is a digital comic format that allows comics to be viewed online. The creation process for webtoons is divided into story planning, content/sketching, coloring, and background drawing. Since each stage of webtoon production requires human intervention, it is both time-consuming and expensive. As a result, deep learning technologies such as automatic coloring and automatic line drawing are being used to reduce human involvement. However, there is a shortage of technology that can assist authors with story creation in webtoon production. Therefore, this study proposes a multilingual text-to-image model capable of generating webtoon images when presented with multilingual input text. The proposed model employs Multilingual BERT to extract feature vectors for multiple languages, and trains a DCGAN in conjunction with the images. The experimental results demonstrate that the model can generate images that are similar to the original images when presented with multilingual input text after training.

Manual review of the extensive literature covering nutrition-based lifestyle interventions to promote healthy cognitive ageing has proved educative, however, data-driven techniques can better account for the large size of the literature (tens of thousands of potentially relevant publications to date) and interdisciplinary nature of where relevant publications may be found. In this study we present a new way to map the literature landscape focusing on nutrition-based lifestyle interventions to promote healthy cognitive ageing. We applied a combination of citation network analysis and text mining to map out the existing literature on nutritional interventions and cognitive health. Results indicated five overarching clusters of publications, which could be further deconstructed into a total of 35 clusters. These could be broadly distinguished by focus on lifespan stages (e.g. infancy versus older age), and specificity regarding nutrition (e.g. narrow focus on iodine deficiency versus broad focus on weight gain). Rather than concentrating into a single cluster, interventions were present throughout the majority of the research. We conclude that a data-driven map of the nutritional intervention literature can benefit the design of future interventions, by highlighting topics and themes that could be synthesized across currently disconnected clusters of publications.

In an era when medical literature is increasing daily, researchers in biomedical and clinical areas have joined efforts with language engineers to analyze large amount of biomedical and molecular biology literature (such as PubMed), patient data or health records. With such a huge amount of reports, evaluating their impact has long seized to be a trivial task. In this context, this paper intends to introduce a non-scientific factor that represents an important element in the effort of gaining acceptance of claims. Thus, we postulate that the confidence the author is expressing in his work plays an important role in shaping the first impression that influences the reader’s perception of the paper. The results discussed in this paper are based on a series of experiments ran over data from the Open Archives Initiative (OAI) corpus that provides interoperability standards in order to facilitate the effectiveness dissemination of the content. This method can be useful to the direct beneficiaries (authors, who are engaged in medical or academic research), but, also, researchers in the fields of BioNLP and NLP, etc.

The research presents the Methodology of Improving the Accuracy in Text Classification in Light of Modelling the Latent Semantic Relations (LSR). The aim of this Methodology is to find the ways of eliminating the Limitations of Discriminant and Probabilistic methods for LSR revealing and customizing the Text Classification Process to the more accurate recognition of the text tonality. This aim should be achieved by using the knowledge about the text’s Hierarchical Semantic Context in the form of Corpora-based Hierarchical Sentiment Dictionary. The main scientific contribution of this research is the following set of approaches to improve the qualitative characteristics of Text Classification process: combination of the Discriminant and Probabilistic methods allowing to decrease the influences of the Limitations of these methods on the LSR revealing process; considering each document as a complex structure allowing to estimate documents integrally by separated classification of topically completed textual component (paragraphs); taking into account the features of Argumentative type of documents (Reviews) allowing to use the author’s subjective evaluation of text tonality for development the Text Classification methodology. Tonality, expressed by the Review’s author, has a significant, but not critical, effect on the qualitative indicators of Sentiment Recognition.

Long unpunctuated texts containing complex linguistic sentences are a stumbling block to processing any low-resource languages. Thus, approaches that attempt to segment lengthy texts with no proper punctuation into simple candidate sentences are a vitally important preprocessing task in many hard-to-solve NLP applications. In this paper, we propose (PDTS) a punctuation detection approach for segmenting Arabic text, built on top of a multilingual BERT-based model and some generic linguistic rules. Furthermore, we showcase how PDTS can be effectively employed as a text tokenizer for unpunctuated documents (i.e., mimicking the transcribed audio-to-text documents). Experimental findings across two evaluation protocols (involving an ablation study and a human-based judgment) demonstrate that PDTS is practically effective in both performance quality and computational cost.

In today's world, news events are emerging incessantly. Classifying the same event reported by different countries is of significant importance for public opinion control and intelligence gathering. Due to the diverse types of news, relying solely on translators would be costly and inefficient, while depending solely on translation systems would incur considerable performance overheads in invoking translation interfaces and storing translated texts. To address this issue, we mainly focus on the clustering problem of cross-lingual news. To be specific, we use a combination of sentence vector representations of news headlines in a mixed semantic space and the topic probability distributions of news content to represent a news article. In the training of cross-lingual models, we employ knowledge distillation techniques to fit two semantic spaces into a mixed semantic space. We abandon traditional static clustering methods like K-Means and AGNES in favor of the incremental clustering algorithm Single-Pass, which we further modify to better suit cross-lingual news clustering scenarios. Our main contributions are as follows: (1) We adopt the English standard BERT as the teacher model and XLM-Roberta as the student model, training a cross-lingual model through knowledge distillation that can represent sentence-level bilingual texts in both Chinese and English. (2) We use the LDA topic model to represent news as a combination of cross-lingual vectors for headlines and topic probability distributions for content, introducing concepts such as topic similarity to address the cross-lingual issue in news content representation. (3) We adapt the Single-Pass clustering algorithm for the news context to make it more applicable. Our optimizations of Single-Pass include adjusting the distance algorithm between samples and clusters, adding cluster merging operations, and incorporating a news time parameter.

Text summarization remains a challenging task in the Natural Language Processing field despite the plethora of applications in enterprises and daily life. One of the common use cases is the summarization of web pages which has the potential to provide an overview of web pages to devices with limited features. In fact, despite the increasing penetration rate of mobile devices in rural areas, the bulk of those devices offer limited features in addition to the fact that these areas are covered with limited connectivity such as the GSM network. Summarizing web pages into SMS becomes, therefore, an important task to provide information to limited devices. This work introduces WATS-SMS, a T5-based French Wikipedia Abstractive Text Summarizer for SMS. It is built through a transfer learning approach. The T5 English pre-trained model is used to generate a French text summarization model by retraining the model on 25,000 Wikipedia pages then compared with different approaches in the literature. The objective is twofold: (1) to check the assumption made in the literature that abstractive models provide better results compared to extractive ones; and (2) to evaluate the performance of our model compared to other existing abstractive models. A score based on ROUGE metrics gave us a value of 52% for articles with length up to 500 characters against 34.2% for transformer-ED and 12.7% for seq-2seq-attention; and a value of 77% for articles with larger size against 37% for transformers-DMCA. Moreover, an architecture including a software SMS-gateway has been developed to allow owners of mobile devices with limited features to send requests and to receive summaries through the GSM network.

In the present, the growth of digitization and worldwide communications make OCR systems of exotic languages a very important task. In this paper, we attempt to develop an OCR system for one of these exotic languages with a unique script, Amharic. Motivated by the recent success of the Attention mechanism in Neural Machine Translation (NMT), we extend the attention mechanism for Amharic text-image recognition. The proposed model consists of CNNs and attention embedded recurrent encoder-decoder networks that are integrated following the configuration of the seq2seq framework. The attention network parameters are trained in an end-to-end fashion and the context vector is injected, with the previously predicted output, at each time steps of decoding. Unlike the existing OCR model that minimizes the CTC objective function, the new model minimizes the categorical cross-entropy loss. The performance of the proposed attention-based model is evaluated against the test dataset from the ADOCR database which consists of both printed and synthetically generated Amharic text-line images and achieved promising results with a CER of 1.54% and 1.17% respectively.

This paper propose an original approach of achieving a Cymatics based visual perception of image-extracted text. In this context, an effective approach for automated text detection and recognition for the natural scene images is proposed. The incoming image is firstly enhanced by employing CLAHE and DWT. Afterwards, the text regions of the enhanced image are detected by employing the MSER feature detector. The non-text MSERs are removed by employing the geometrical and contour based filters. The remaining MSERs are grouped into words or phrases by finding out similarities between them. The text recognition is performed by employing an OCR function. The extracted text is sequentially analysed on character by character basis. Each character is converted into a methodical acoustic excitation. Finally, these excitations are converted into the systematic visual perceptions by using the phenomenon of Cymatics. The system functionality is tested with an experimental setup. For the case of studied natural scenes, the suggested approach achieves 80% precision in text localization and 53% precision in end-to-end text recognition. The devised system principle is novel and can be employed in various applications like visual art, encryption, education, integration of impaired people, etc.

(1) Background: Breast cancer is a frequent heterogeneous disorder diagnosed in woman and is a high cause of mortality of them in reason to rapid metastasis and disease recurrence. Ferroptosis can inhibit breast cancer cell growth, improve the sensitivity of chemotherapy and radiotherapy and inhibit distant metastases so potentially acts on tumor micro-environment; (2) Methods: Ferroptosis/Extracellular matrix remodeling literature text-mining results were integrated in breast cancer transcriptome cohort according their distant relapse free survival (DRFS) under adjuvant therapy (anthracyclin+taxanes) and also in MDA-MB-231 transcriptome functional experiments with ferroptosis activations (GSE173905); (3) Results: Ferroptosis/Extracellular matrix remodeling text-mining identified 910 associated genes in at list 10 articles. Univariate Cox analyses censored on breast cancer (GSE25066) selected 252 individual significant genes and 171 of them found with an adverse expression. Functional enrichment of these 171 adverse genes predicted basal breast cancer signatures. By text-mining some ferroptosis significant adverse selected genes shared citations in domain of ECM remodeling such as: TNF, IL6, SET, CDKN2A, EGFR, HMGB1, KRAS, MET, LCN2, HIF1A, TLR4. A molecular score based on expression the eleven genes was found predictive of worst prognosis breast cancer at univariate level: basal subtype, short DRFS, high grade values 3 and 4, estrogen and progesterone receptors negative and nodal stages 2 and 3. This eleven gene signature was validated as regulated by ferroptosis inductors (erastin and RSL3) in triple negative breast cancer cellular model MDA-MB-231.; (4) Conclusions: Crosstalk between ECM remodeling-Ferroptosis functionalities allowed to define a molecular score which have been characterized as an independent adverse parameter in prognosis of breast cancer patients. Gene signature of this molecular score have been validated to be regulated by erastin/RSL3 ferroptosis activators. This molecular score could be promising to evaluate ECM impact of ferroptosis target therapies in breast cancer.

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%. The new ANDDigest version (01.2022) has a web interface and is freely available to users at https://anddigest.sysbio.ru/.

The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning text mining algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm is trained by TFIDF bigram features which contribute a network training model. The algorithm is tested on two different real-world datasets from the CBC news network and Covid-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97-99 %. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents which may contribute negatively to the COVID-19 pandemic.

A global event such as the COVID-19 crisis presents new, often unexpected responses that are fascinating to investigate from both, scientific and social standpoints. Despite several documented similarities, the Coronavirus pandemic is clearly distinct from the 1918 flu pandemic in terms of our exponentially increased, almost instantaneous ability to access/share information, offering an unprecedented opportunity to visualise rippling effects of global events across space and time. Personal devices provide “big data” on people’s movement, the environment and economic trends, while access to the unprecedented flurry in scientific publications and media posts provides a measure of the response of the educated world to the crisis. Most bibliometric (co-authorship, co-citation, or bibliographic coupling) analyses ignore the time dimension, but COVID-19 has made it possible to perform a detailed temporal investigation into the pandemic. Here, we report a comprehensive network analysis based on more than 20000 published documents on viral epidemics, authored by over 75,000 individuals from 140 nations in the past one year of the crisis. In contrast to the 1918 flu pandemic, access to published data over the past two decades enabled a comparison of publishing trends between the ongoing COVID-19 pandemic and those of the 2003 SARS epidemic, to study changes in thematic foci and societal pressures dictating research over the course of a crisis.

This study aims to understand the global environment of COVID-19 management and guide future policy directions after the pandemic crisis. To this end, we analyzed a series of the World Economic Forum’s COVID-19 response reports through text mining and network analysis. These reports, written by experts in diverse fields, discuss multidimensional changes in socioeconomic situations, various problems created by those changes, and strategies to respond to national crises. Based on 3,897 refined words drawn from a morphological analysis of 26 reports (as of the end of 2020), this study analyzes the frequency of words, the relationships among words, the importance of specific documents, and the connection centrality through text mining. In addition, network analysis helps develop strategies for sustainable response to and management of national crises through identifying clusters of words with a similar structural equivalence.

In the realm of the five-category classification endeavor, there has been limited exploration into applied techniques for classifying Arabic text. These methods have primarily leaned on singletask learning, incorporating manually crafted features that lack robust sentence representations. Recently, the Transformer paradigm has emerged as a highly promising alternative. However, when these models are trained using single-task learning, they often face challenges in achieving outstanding performance and generating robust latent feature representations, especially when dealing with small datasets. This issue is particularly pronounced in the context of the Arabic dialect, which has a scarcity of available resources. Given these constraints, this study introduces an innovative approach to dissecting sentiment in Arabic text. This approach combines Inductive Transfer (INT) with the Transformer paradigm to augment the adaptability of the model and refine the representation of sentences. By employing self-attention SE-A and feed-forward sub-layers as a shared Transformer encoder for both the five-category and three-category Arabic text classification tasks, this proposed model adeptly discerns sentiment in Arabic dialect sentences. The empirical findings underscore the commendable performance of the proposed model, as demonstrated in assessments of the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

The article aims to explore social aspects in the area of energy research by using bibliometrics methods and tools. The study is based on the data of Energy Research & Social Science journal publications exported from ScienceDirect database during 2019-2023. Methodologically, the work included clustering of the bibliometric records under consideration. This allowed to identify the main topics of publications and those in which the research of social problems prevails. This stage of the work was carried out using carrot2, an engine for clustering search results. Next, the analysis of publication topics related to social issues research was carried out using VOSviewer — a software tool for visualizing scientific landscapes. The topics of the four clusters obtained at this stage were described by key terms with the maximum total link strength. For the principal ones, a definition was made on the basis of the texts of publications that most fully disclose the meaning of these terms. The 1537 publications in the journal during the period under review deal with many aspects of energy transition research, renewable energy, energy policy, energy justice and energy systems, but only 78 of them are dominated by social issues. The paper reveals the main social issues in the field of energy research: sociology of energy, socio-technical system, community energy, energy democracy, sociology of the future, energy sovereignty, socio-technical imaginary and shows the insufficient attention to this topic, especially to the investment of such research.

Requirements are predominantly written in Natural Language (NL), which makes them accessible to stakeholders with varying degrees of experience, as compared to a model-based language which requires special training. However, despite its benefits, NL can introduce ambiguities and inconsistencies in requirements, which may eventually result in system quality degradation and system failure altogether. The system complexity that characterizes current systems warrants an integrated and comprehensive approach to system design and development. This need has brought about a paradigm shift towards Model-Based Systems Engineering (MBSE) approaches to system design and a departure from traditional document-centric methods. While, MBSE shows great promise, the ambiguities and inconsistencies present in NL requirements hinder their conversion to models directly. The field of Natural Language Processing (NLP) has demonstrated great potential in facilitating the conversion of NL requirements into a semi-machine-readable format that enables their standardization and use in a model-based environment. A first step towards standardizing requirements consists of classifying them according to the ``type'' (design, functional, performance, etc.) they represent. To that end, a language model capable of classifying requirements needs to be fine-tuned on labeled aerospace requirements. This paper presents the development of an annotated aerospace requirements corpus and its use to fine-tune BERT$_\text{BASE-UNCASED}$ to obtain aeroBERT-Classifier: a new language model for classifying aerospace requirements into design, functional, or performance requirements. A comparison between aeroBERT-Classifier and bart-large-mnli (zero-shot text classification) showcased the superior performance of aeroBERT-Classifier on classifying aerospace requirements despite being fine-tuned on a small labeled dataset.

Introduction: Around a million people are reported to die by suicide every year, and due to the stigma associated with the nature of the death, this figure is usually assumed to be an underestimate. Suicide may be prevented if prompt intervention is taken to mitigate risk. Machine learning and artificial intelligence-based modelling, such as natural language processing (NLP) and other text analytics approaches, has the potential to become a major technique for the detection, diagnosis, and treatment of people who are suffering from mental health issues. The primary aims of this research are to determine whether NLP techniques have been utilised in the field of suicide prevention, and if so, were they effective? What were their limitations? Methods: PubMed, EMBASE, MEDLINE, PsycInfo, and Global Health databases were searched for studies that reported use of NLP for suicide ideation or self-harm. Thematic analysis was used to synthesise and analyse the included studies. Findings were reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, and the Mixed Methods Appraisal Tool (MMAT) was used in assessing paper quality. Result: The preliminary search of five databases generated 387 results. Removal of duplicates resulted in 158 potentially suitable studies. Twenty papers were finally included in this review. Discussion: Studies show that combining structured and unstructured data in NLP data modelling yielded more accurate results than utilizing either alone. Also, to reduce suicides, people with mental problems must be continuously and passively monitored. Further, NLP and other machine learning/artificial intelligence technologies can be used to address health inequities and electronic health records provide valuable data for creating suicide risk tools. Finally, Online, social media, and smartphone applications can be leverage in detecting people with suicide ideation. Conclusion: The use of artificial intelligence and machine learning opens new avenues for considerably guiding risk prediction and advancing suicide prevention frameworks. The review's analysis of the included research revealed that the use of NLP may result in low-cost and effective alternatives to existing resource-intensive methods of suicide prevention. To summarise, there is substantial evidence that NLP is useful in identifying people who have suicide ideation.

Nowadays, an analysis of virtual media to predict society’s reaction to any events or processes is a task of great relevance. Especially it concerns meaningful information on healthcare problems. Internet sources contain a large amount of pharmacologically meaningful information useful for pharmacovigilance purposes and repurposing drug use. An analysis of such a scale of information demands developing the methods that require the creation of a corpus with labeled relations among entities. Before, there have been no such Russian language datasets. This paper considers the first Russian language dataset where labeled entity pairs are divided into multiple contexts within a single text (by used drugs, by different users, by the cases of use, etc.), and a method based on the XLM-RoBERTa language model, previously trained on medical texts to evaluate the state-of-the-art accuracy for the task of indication of the four types of relationships among entities: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown based on the presented dataset from the Russian Drug Review Corpus, the developed method achieves the F1-score of 81.2% (obtained using cross-validation and averaged for the four types of relationships), which is 7.8% higher than the basic classifiers.

Study question: What are the current trends of research in Human Assisted Reproduction around the world? Summary answer: USA is the leading country, followed by the UK, China, France and Italy. The largest research area is “laboratory techniques”, although other areas such as “public health”, “quality, ethics and law” and “female factor” are gaining ground worldwide. What is known already: Scientific research, especially in health and medical sciences, aims at addressing specific needs that society (and, especially, patients) perceives as pressing. One of the main challenges for policymakers and research funders alike is therefore to align research priorities to societal needs. We can thus think of research agendas in terms of a demand side (societal needs) and a supply side (research outputs). Research output in Human Assisted Reproduction has expanded in the past years, as indicated by the increasing number of scientific publications in indexed journals in this area. Nevertheless, no map of research related to assisted reproduction has been produced so far, hindering the identification of potential areas of improvement and need. Study design, size, duration: 26,000+ scientific publications (articles, letters, and reviews) on Human Assisted Reproduction produced worldwide between 2005 and 2016 were analyzed. These publications were indexed in PubMed or obtained from reference list of indexed publications included in the analysis.Participants/materials, setting, methods: The corpus of publications was obtained by combining the MeSH terms: “Reproductive techniques”, “Reproductive medicine”, “Reproductive health”, “Fertility”, “Infertility”, and “Germ cells”. Then it was analyzed by means of text mining algorithms (Topic Modeling (TM) based on Latent Dirichlet Allocation (LDA)), in order to obtain the main topics of interest. Finally, these categories were analyzed across world regions and time. Main results and the role of chance: We identified 44 main topics, which were further grouped in 11 macro categories, form larger to smaller: “laboratory techniques”, “male factor”, “quality, ethics and law”, “female factor”, “public health and infectious diseases”, “basic research and genetics”, “pregnancy complications and risks”, “general infertility and ART”, “psychosocial aspects”, “cancer”, and “research methodology”. The USA was the leading country in number of publications, followed by the UK, China, France and Italy. Interestingly, research contents in high income countries is fairly homogeneous across macro-categories, and it is dominated by “laboratory techniques” in Western and Southern Europe, and by “quality, ethics and law” in North America, Australia and New Zealand. In middle income countries we observe that research is mainly performed on “male factor”, and noticeably less on “female factor”. Finally, research on “public health and infectious diseases” predominates in low-income countries. Regarding temporal evolution of research, “laboratory techniques” is the most abundant topic on a yearly basis, and relatively constant over time. However, since production in most of the other categories is increasing, the relative contribution of this research category is actually decreasing. Publication is especially increasing in “public health and infectious diseases” (in all world regions, but especially in low income countries), “quality, ethics and law” (high income countries), and “female factor” (middle income countries). Limitations, reasons for caution: Three main factors might limit the robustness of our work: the textual corpus analyzed is based on abstract and titles, the reproducibility of the stochastic algorithms applied, which may produce slightly differing results at each run, and the interpretation of the topics obtained. Wider implications of the findings: This study should prove beneficial in the design of research strategies and policies that foster the alignment between supply (assisted reproduction research) and demand (society). Study funding/competing interest(s): PTQ-14-06718 of the Spanish MINECO Torres Quevedo programme (FAM).

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6,497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain the text-based risk score algorithm as predictive of death within 30 days of surgery. We studied the additional performance obtained by including text-based risk score as a predictor of death along with other structured data based clinical risk factors. The C-statistic of a logistic regression model with 5-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.

With the increasing use of cloud-based services for training and deploying machine learning models, data privacy has become a major concern. This is particularly important for natural language processing (NLP) models, which often process sensitive information such as personal communications and confidential documents. In this study, we propose a method for training NLP models on encrypted text data to mitigate data privacy concerns while maintaining similar performance to models trained on non-encrypted data. We demonstrate our method using two different architectures, namely Doc2Vec+XGBoost and Doc2Vec+LSTM, and evaluate the models on the 20 Newsgroups dataset. Our results indicate that both encrypted and non-encrypted models achieve comparable performance, suggesting that our encryption method is effective in preserving data privacy without sacrificing model accuracy. In order to replicate our experiments, we have provided a Colab notebook at the following address: https://t.ly/lR-TP

In this paper, we introduce a model for the automatic generation of literary sentences in French. It is based on algorithms that we have previously used to generate sentences in Spanish and Portuguese, and on a new corpus consisting of literary texts in French that we have constructed, called [FR]. Our automatic text generation algorithm combines language models, shallow parsing and deep learning, artificial neural networks. We have also proposed and implemented a manual evaluation protocol to assess the quality of the artificial sentences generated by our algorithm, by testing if they fulfill four simple criteria. We have obtained encouraging results from the evaluators for most of the desired features of our artificially generated sentences.

The ability to identify and rank risk is essential for efficient and effective supervision of financial service firms, such as banks and insurers. Risk ranking ensures limited resources are allocated where they are most needed. Today, automatic risk identification within insurance supervision primarily relies on quantitative metrics based on numerical data (e.g. returns). The purpose of this work is to assess whether Natural Language Processing (NLP) and cognitive networks can achieve similar automated risk ranking and identification by analysing textual data, i.e. NIDT=829 investor transcripts from Bloomberg. To this aim, this work explores and tunes 3 NLP techniques: (1) keyword extraction enhanced by cognitive network analysis; (2) valence/sentiment analysis; and (3) topic modelling. Results highlight that keyword analysis, enriched by term frequency-inverse document frequency scores and semantic framing through cognitive networks, could detect events of relevance for the insurance system like cyber-attacks or the COVID-19 pandemic. Cognitive networks were found to highlight events that related to specific financial transitions: The semantic frame of "climate" grew in size by +538% between 2018 and 2020 and outlined an increased awareness that agents and insurers expressed towards climate change. A lexicon-based sentiment analysis achieved a Pearson’s correlation of ρ=0.16 (p<0.001,N=829) between sentiment levels and daily share prices. Although relatively weak, this finding indicates that insurance jargon is insightful to support risk supervision. Topic modelling is considered less amenable to support supervision, because of a lack of results’ stability and an intrinsic difficulty to interpret risk patterns. We discuss how these automatic methods could complement existing supervisory tools in automated risk ranking.

In the current business environment, firms are eager to adopt new technology as they witness and perceive more and more successful business applications of new technologies, e.g., Big Data, Artificial Intelligence (A.I.), Cloud Computing, etc. As one of the disruptive technologies, Blockchain technology (BCT) is now drawing public attention owing to the cryptocurrency phenomenon (e.g., Bitcoin), for which Blockchain serves as the backbone technology. Given certain innovative features of BCT, especially its transparency, traceability, security, efficiency, confidentiality, and immutability, BCT holds out the promise of impacting supply chain operational and financial efficiencies. Recently, the burgeoning of Metaverse and Non-Fungible Token (NFT) has boosted the BCT as state-of-the-art technology to another notch. Motivated by the proliferating adoption of BCT, we conduct a holistic literature review with a focus on the status of research on BCT in the context of Supply Chain Management (SCM). In particular, this Blockchain-centered research reviews the research up to date on the Blockchain application in SCM. It provides holistic review in terms of (1) the functionality of BCT and its salient features; (2) the prevailing and potential applications of BCT; (3) and the business benefits and impact of BCT in SCM. Finally, we substantiate and highlight a variety of research directions.

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

In this paper, we propose a novel approach of video skimming by exploiting the fusion of video temporal information and keyword information representation extracted from multi-model video information including audio, text and visual indices. In addition, we introduce the brand-safe filtering and sentiment analysis in order to only reserve the user-friendly content in the video skim. In the experiment by using the videos from YouTube-8M dataset, we have proved that the semantic conservation in the video skim from the proposed approach highly outperforms the approaches by only partial information of the video in conserving the semantic content of the video.

This paper proposes two fully-automatic machine-extracted linguistic features from an unlimited text input for Mandarin prosody generation. One is the punctuation confidence (PC) which measures the likelihood of inserting a major punctuation mark (PM) at a word boundary. Another is the quotation confidence (QC) which measures the likelihood of a word string to be quoted as a meaningful or emphasized unit in text. Because a major PM in a text is highly correlated with a prosodic break, and a quoted word string plays an important role in human language understanding, the two features potentially could provide useful information for prosody generation. The idea is first realized by employing conditional random field (CRF)-based models to predict major PMs, quoted word string locations, and their associated confidences, i.e., the PC and the QC, for each word boundary. Then, the predicted punctuations and their confidences are combined with traditional contextual linguistic features to predict prosodic-acoustic features. Both objective and subjective tests showed that the prosody generation with the proposed linguistic features performed better than the one without the proposed features. So, the proposed PC and QC are promising features for Mandarin prosody generation.

Binarization of degraded documents is an important preprocessing task for various document analysis such as OCR and historical document analysis. Existing studies have applied various convolutional neural network (CNN) models and generative models for document binarization, but they do not show generalized performance for noise that the model has not seen and it suffers from extracting elaborate text strokes. In this paper, to overcome these challenges, we utilize latent diffusion model (LDM), which is known for high-quality image generation model, for the first time in document binarization. By utilizing the iterative diffusion-denoising process in latent space, it shows high-quality cleaned binarized image generation and high generalized performance through using both data distribution and time step while training. Additionally, we apply gated U-Net to the backbone network to preserve text strokes using trainable gating value. Gated convolution can extract elaborate text stroke by allowing the model to focus on text region by combining gating value and feature. Furthermore, we maximize the effectiveness of the proposed model by training it with a combination of LDM loss and pixel-level loss, which is suitable for the model structure. Experiments on H-DIBCO and DIBCO benchmark datasets show that the proposed model outperforms existing methods.

This paper considers the reasonability of using GSDMM as a method for clustering short texts - titles and abstracts of publications 2021-2023 of MDPI journals on sustainable energy topics. The paper proposes an approach to identifying relevant research topics based on the use of the Python script Yake!, the Krovetz streamer, the GSDMM algorithm, and short text annotations. It is emphasized that researchers prefer to rely on specific publications rather than keywords when searching for information on a topic of interest. The bibliometric records of sustainable energy publications from 2021 to 2023 in Sustainability (Energy Sustainability section — 1,926 search results) and Energies (Sustainable Energy section — 994 search results) were used as data for analysis. The GSDMM algorithm was used to cluster the texts, and Yake! to extract the keywords for the GSDMM algorithm's vocabulary. This article summarizes topics found in 9 clusters, including general sustainable energy issues, biomass recycling, region-specific issues, building energy use, economic growth and investment, numerical flow modeling, generation cost optimization, heat to energy conversion, weather-related risks, and false data attacks.

Industrial nameplates serve as a means of conveying critical information and parameters. In this work, we propose a novel approach for rectifying industrial nameplate pictures utilizing a probabilistic Hough transform. Our method effectively corrects for distortions and clipping, and features a collection of challenging nameplate pictures for analysis. To determine the corners of the nameplate, we employ a progressive probability Hough transform, which not only enhances detection accuracy but also possesses the ability to handle complex industrial scenarios. The results of our approach are clear and readable nameplate text, as demonstrated through experiments that show improved accuracy in model identification compared to other methods.

This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for Word Representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a strong impact on the environment.

For any molecule, network, or process of interest, to keep up with new publications on these, is becoming increasingly difficult. For many cellular processes, molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large scale molecular interaction maps and database curation. Text mining and Natural Language Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and machine learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention based models, a special type of neural network (NN)-based architectures that have recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at a sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conduct a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.

Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, the different quality of language use across sources, present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research, aimed at the Southern Balkans and Coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “Expert-Apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a Machine Learning (ML) algorithm, utilizing a combination of Active Learning (AL) and Reinforcement Learning (RL), and the Expert is the human researcher. ML-powered research improved 3.1 times the effectiveness and 5.14 times the efficiency of the domain expert, fetching a total number of 420 relevant ethnopharmacological documents in only 7 hours versus an estimated 36-hour human-expert effort. Therefore, utilizing Artificial Intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

This article presents a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing pre-processing which includes removing columns containing user information, retweet’s count, followers information, duplicate tweets, removing unnecessary punctuation, links, symbols, and spaces, and finally extracting emojis if present in the tweet text. In the final dataset each tweet record contains columns for tweet id, text, and emoji extracted from the text with a sentiment score. Emojis are extracted to validate Machine Learning models used for the multilingual sentiment and behavior analysis. These are extracted using a Python script that searches for an emoji from the list of 751 most frequently used emojis. If an emoji is present in the text, a column with the emoji description and sentiment score is added.

The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.

Recent state-of-the-art scene text recognition methods have primarily focused on horizontal text in images. However, in several Asian countries, including China, large amounts of text in signs, books, and TV commercials are vertically directed. Because the horizontal and vertical texts exhibit different characteristics, developing an algorithm that can simultaneously recognize both types of text in real environments is necessary. To address this problem, we adopted the direction encoding mask (DEM) and selective attention network (SAN) methods based on supervised learning. DEM contains directional information to compensate in cases that lack text direction; therefore, our network is trained using this information to handle the vertical text. The SAN method is designed to work individually for both types of text. To train the network to recognize both types of text and to evaluate the effectiveness of the designed model, we prepared a new synthetic vertical text dataset and collected an actual vertical text dataset (VTD142) from the Web. Using these datasets, we proved that our proposed model can accurately recognize both vertical and horizontal text and can achieve state-of-the-art results in experiments using benchmark datasets, including the street view test (SVT), IIIT-5k, and ICDAR. Although our model is relatively simple as compared to its predecessors, it maintains the accuracy and is trained in an end-to-end manner.

Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has several drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness. In addition, annotators may annotate ambiguous data. So, we developed the concept of automated annotation to get the best annotations using several machine-learning approaches. The proposed approach is based on an ensemble algorithm of meta-learners and meta-vectorizer techniques. The approach employs a semi-supervised learning technique for automated annotation, aimed at detecting hate speech. This involves leveraging various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB), in conjunction with Word2Vec and TF-IDF text extraction methods. The annotation process is performed using 13,169 Indonesian YouTube comments data. The proposed model used a Stemming approach using data from Sastrawi and also new data of 2,245 words. Semi-supervised learning uses 5%, 10%, and 20% of labeled data as compared to performing labeling based on 80% of the datasets. In semi-supervised learning, the model learns from the labeled data, which provides explicit information, and the unlabeled data, which offers implicit insights. This hybrid approach enables the model to generalize and make informed predictions even when limited labeled data is available, ultimately enhancing its ability to handle real-world scenarios with scarce annotated information. In addition, the proposed method uses a variety of thresholds for matching words labeled with hate speech ranging from 0.6, 0.7, 0.8, and 0.9. The experiment showed that the KNN-Word2ec model has the best accuracy value of 96.9% with a scenario of 5%:80%:0.9. However, several other methods have also accuracy above 90%, such as SVM and DT based on both text extraction methods in several test scenarios.

In recent years, the high-frequency abnormal price fluctuations of small-scale agricultural products have been detrimental to market stability. The traditional method based on statistical data of production factors can no longer accurately predict abnormal price fluctuations. Taking scallions as a case, the paper identifies price influencing factors from massive network information, and analyzes the causes and changing characteristics of price fluctuations from four perspectives: market supply, economic environment and market sentiment, and attention. Initially, the study employs an LDA topic model to extract factors from online sentiment data over the past four years, including market supply and demand, economic conditions, related agricultural prices, and market attention. Subsequently, using the SO-PMI algorithm to expand domain-specific lexicons and employing semantic and sentiment analysis with syntactic parsing, the study aims to improve the accuracy of sentiment quantification in text analysis. Furthermore, cointegration and Granger causality tests validate the significant impact of these factors on scallion price fluctuations, mitigating spurious regression issues. Finally, by employing a TVP-VAR model, the study compares the impulse responses and shock magnitudes of various factors in different time and spatial dimensions. It also delves into the mechanisms of heterogeneous impacts and trends in scallion prices, considering the corresponding socio-economic context and public sentiment events within specific time windows, thereby providing valuable decision-making insights for the healthy development of agricultural markets.

This study examined the eye movement patterns of 317 elementary students across reading conditions (audio-assisted reading (AR) and reading-only (R)) and text types (fiction and non-fiction), and identified eye movement parameters that predict their literal comprehension (LC) and inferential comprehension (IC). Participants, randomly assigned to either reading condition and either text type, answered questions assessing their LC and IC. Average fixation duration (AFD), total fixation duration (TFD), and scanpath length were used as eye movement parameters. There were main effects of age on all parameters and interaction effects between age and reading condition on TFD and scanpath length. Controlling for age, TFD had a positive impact on LC of both text types in the AR, while under the R, it affected negatively on IC of both text types. Longer scanpaths predicted IC of fiction in the AR, LC and IC of non-fiction under the AR, and LC of non-fiction within the R. AFD influenced negatively on IC of fiction in the AR, LC and IC of non-fiction in the AR, and LC of non-fiction under the R. These eye movement patterns and predictors indicate children employ different reading strategies based on reading modality and text type.

Background: Food insecurity is an ongoing problem in the U.S. with implications for health problems and social disadvantages. Past food insecurity intervention studies have targeted barriers to accessing healthy food. Although, information barriers have not been adequately addressed by food insecurity interventions. Methods: This qualitative study involved testing a text message as an informational nudge through interviews among adults in the southwest U.S. (n = 10). Interview questions focused on the message content, persuasiveness, relevancy, clarity, and details that participants liked and disliked. A qualitative content analysis of interview responses then highlighted any necessary modifications to the text message. Results: Interview responses highlighted that the message conveyed details about how to access food assistance resources. The message was perceived as informative, clear, and friendly. Participants highlighted the need for better spacing in the message layout, more details about the Supplemental Nutrition Assistance Program (SNAP), and why SNAP is relevant. Conclusion: Text message readability and informativeness were common concerns raised during interviews. Participant-informed modifications to the text message were applied in preparation for using the message in a food insecurity intervention. Future food insecurity research efforts should similarly test novel interventions to ensure the intervention is acceptable for the target population.

Google play store allow the user to download a mobile application (app) and user get inspired by the rating and reviews of the mobile app. A recent study analyzes that user preferences, user opinion for improvement, user sentiment about particular feature and detail with descriptions of experiences are very useful for an application developer. However, many application reviews are very large and difficult to process manually. Star rating is given of the whole application and the developer cannot analyze the single feature. In this research, we have scrapped 282,231 user reviews through different data scraping techniques. We have applied the text classification on these user reviews. We have applied different algorithms and find the precision, accuracy, F1 score and recall. In evaluated results, we have to also find the best algorithm.

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN works best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features.

Tourism is an important industry that promotes national economic and social progress. All-for-one tourism is a new concept of regionally coordinated development that uses the tourism industry as an engine to boost resource integration, industrial integration, and social sharing. Tourism service is the main embodiment of cultural ecosystem services for all-for-one tourism cities. Taking Liyang in China as an example, this paper used a combination of GIS spatial analysis and big data text mining to evaluate the tourism service from three aspects: the quality of tourism resources, the comprehensiveness of tourism service facilities, and the satisfaction of tourists. The results show that (1) the tourism service is better in the northwestern and eastern areas of the city, while it is lower in the northeastern and southwestern parts; (2) the hotspot areas should focus on improving the tour routes, the transport capacity, and excessive charges; the cold spot areas should work on ecological restoration and creating new tourism attractions by combining the local industries; (3) rural tourism integrating agriculture and visitation should be highlighted as a key growth point to improve the city’s tourism service function.

Wide-scale commercialization of cultured meat, produced from in vitro-grown animal stem cells, is still a long way off because of technical, regulatory and, above all, social acceptability constraints. As a result, despite the advancement of knowledge, it is difficult to predict whether this innovation will be commonly accepted. The concept of media framing is key to better understanding individuals’ decision-making and mental interpretative patterns in relation to cultured meat adoption. This research therefore analyzes the role of media framing (namely the social media platform Twitter) on the components of consumer attitudes (cognitive, affective, and conative) in relation to cultured meat. A total of 23 020 publications and 38 531 comments were analyzed qualitatively (content analysis) and quantitatively (MANOVA). This study showed that media-framed posts influenced consumer attitudes more than did non-media-framed posts. Although the results indicate that the different types of media framing (ethical, intrinsic, informational, and belief) do not exert the same influence on each attitude component, they suggest that posts combining the ethical, intrinsic, and informational media frames have a greater impact on the acceptability of cultured meat and that the belief frame is equally important, particularly for the behavioral component. Relevant implications can be drawn for authorities and businesses on the use of differentiated education and marketing strategies

The use of artificial intelligence in natural language processing (NLP) has significantly contributed to the advancement of natural language applications such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources such as webpages, blogs, emails, social media, and articles, one of the most common tasks in natural language processing is the classification of a text corpus. This is important in many institutions for planning, decision-making, and archives of their projects. Many algorithms exist to automate text classification operations but the most intriguing of them is that which also learns these operations automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables the reduction of the curse of dimensionality in text classification. The classification results are compared with those of conventional text classification models, and it shows that our proposed model has a higher classification performance than conventional models.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. Their main feature is their ability to pattern recognition, optimization, or image segmentation. However, sometimes it is not easy to provide the users with good explanations about the results obtained with them due to mainly the large number of changes in the state of neurons (and their weights) produced during a problem of machine learning. There are currently limited techniques to visualize, verbalize, or abstract HNNs. This paper outlines how we can construct automatic video generation systems to explain their execution. This work constitutes a novel approach to get explainable artificial intelligence systems in general and HNNs in particular building on the theory of data-to-text systems and software visualization approaches. We present a complete methodology to build these kinds of systems. Software architecture is also designed, implemented, and tested. Technical details about the implementation are also detailed and explained. Finally, we apply our approach for creating a complete explainer video about the execution of HNNs on a small recognition problem.

Background: HIV poses a threat to global health. With effective treatment options available, education and testing strategies are essential in preventing transmission. Text messaging is an effective tool for health promotion and can be used to target higher risk populations. This study reports on the design, delivery and testing of a mobile text messaging SMS intervention for HIV prevention and awareness, aimed at adults in the construction industry and delivered during the COVID-19 pandemic. Method: Participants were recruited at Test@Work workplace health promotion events (21 sites, n=464 employees), including health checks with HIV testing. Message development was based on a participatory design and included a focus group (n=9) and message fidelity testing (n=291) with assessment of intervention uptake, reach, acceptability, and engagement. Barriers to HIV testing were identified and mapped to the COM-B behavioural model. 23 one-way push SMS messages (19 included short web links) were generated and fidelity tested, then sent via automated SMS to two employee cohorts over a 10-week period during the COVID-19 pandemic. Engagement metrics measured were; opt-outs, SMS delivered/read, number of clicks per web link, and four two-way pull messages exploring repeat HIV testing, learning new information, perceived usefulness and behaviour change. Results: 291 people participated (68.3% of eligible attendees). A total of 7,726 messages were sent between March and June 2020, with 91.6% successfully delivered (100% read). 12.4% of participants opted out over 10 weeks. Of delivered messages, links were clicked an average of 14.4%, max 24.1% for HIV related links. The number of clicks on web links declined over time (r= -6.24, p=0.01). Response rate for two-way pull messages was 13.7% of participants. Since the workplace HIV test offer at recruitment, 21.6% reported having taken a further HIV test. Qualitative replies indicated behavioural influence of messaging on exercise, lifestyle behaviours and intention to HIV test. Conclusion: SMS messaging for HIV prevention and awareness is acceptable to adults in the construction industry, has high uptake, low attrition and good engagement with message content, when delivered during a global pandemic. Data collection methods may need refinement for audience and effect of COVID-19 on results is yet to be understood.

Colonoscopy is used for colorectal cancer (CRC) screening. Extracting details of the colonoscopy findings from free text in electronic health records (EHRs) can be used to determine patient risk for CRC and colorectal screening strategies. In this study, we developed and evaluated the accuracy of a deep learning model framework to extract information for the clinical decision support system to analyze relevant free-text colonoscopy reports, including indications, pathology, and findings notes. The Bio-Bi-LSTM-CRF framework was developed using Bidirectional Long Short-term Memory (Bi-LSTM) and Conditional Random Fields (CRF) to extract several clinical features from these free-text reports, including indications for the colonoscopy, findings during the colonoscopy, and pathology of the resected material. We then trained the Bio-Bi-LSTM-CRF and existing Bi-LSTM-CRF models on 80% of 4,000 manually annotated notes obtained from the colonoscopy reports of 3,867 patients. The clinical notes were from a group of patients aged over 40 years old enrolled in four Veterans Affairs Medical Centers. A total of 10% of the remaining annotated notes were used to train hyperparameter, while the remaining 10% were used to evaluate the accuracy of our model (Bio-Bi-LSTM-CRF) and to compare the results with the outcomes obtained using Bi-LSTM-CRF. The results of our experiments showed that the bidirectional encoder representations by integrating dictionary function vector from Bio-Bi-LSTM-CRF and strategies character sequence embedding approach is an effective way to identify colonoscopy features from EHR-extracted clinical notes. Therefore, the Bio-Bi-LSTM-CRF model is concluded to be capable of creating new opportunities to identify patients at risk for colon cancer and to study their health outcomes.

Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

Electronic text stylometry is a collection of forensics methods that analyze the writing styles of input electronic texts in order to extract information about authors of the input electronic texts. Such extracted information could be the identity of the authors, or aspects of the authors, such as their gender, age group, ethnicity, etc. This survey paper presents the following contributions: 1) A description of all stylometry problems in probability terms, under a unified notation. To the best of our knowledge, this is the most comprehensive definition to date. 2) A survey of key methods, with a particular attention to data representation (or feature extraction) methods. 3) An evaluation of 23,760 feature extraction methods, which is the most comprehensive evaluation of feature extraction methods in the literature of stylometry to date. The importance of this evaluation is two fold. First, identifying the relative effectiveness of the features (since, currently, many are not evaluated jointly; e.g. syntactic n-grams are not evaluated against k-skip n-grams, and so forth). Second, thanks to our generalizations, we could evaluate novel grams, such as what we name compound grams. 4) The release of our associated Python feature extraction library, namely Fextractor. Essentially, the library generalizes all existing n-gram based feature extraction methods under the "at least l-frequent, dir-directed, k-skipped n-grams'', and allows grams to be diversely defined, including definitions that are based on high-level grammatical aspects, such as POS tags, as well as lower-level ones, such as distribution of function words, word shapes, etc. This makes the library, by far, the most extensive in this domain to date. 5) The construction, evaluation, and release of the first dataset for Emirati social media text. This evaluation represents the first evaluation of author identification against Emirati social media texts. Interestingly, we find that, when using our models and feature extraction library (Fextractor), authors could be identified significantly more accurately than what is reported with similarly sized datasets. The dataset also contains sub-datasets that represent other languages (Dutch, English, Greek and Spanish), and our findings are consistent across them.

The work of this paper presents multiple novel findings from a comprehensive analysis of about 150,000 tweets about exoskeletons posted between May 2017 and May 2023. First, findings from content analysis and temporal analysis of these tweets reveal the specific months per year when a significantly higher volume of Tweets was posted and the time windows when the highest number of tweets, the lowest number of tweets, tweets with the highest number of hashtags, and tweets with the highest number of user mentions were posted. Second, the paper shows that there are statistically significant correlations between the number of tweets posted per hour and different characteristics of these tweets. Third, the paper presents a multiple linear regression model to predict the number of tweets posted per hour in terms of these characteristics of tweets. The R2 score of this model was observed to be 0.9540. Fourth, the paper reports that the 10 most popular hashtags were #exoskeleton, #robotics, #iot, #technology, #tech #innovation, #ai, #sci, #construction and #news. Fifth, sentiment analysis of these tweets was performed using VADER and the DistilRoBERTa-base library. The results show that the percentage of positive, neutral, and negative tweets were 46.8%, 33.1%, and 20.1%, respectively. The results also show that in the tweets that did not express a neutral sentiment, the sentiment of surprise was the most common sentiment. It was followed by the sentiments of joy, disgust, sadness, fear, and anger. Furthermore, analysis of hashtag-specific sentiments revealed several novel insights, for instance, for almost all the months in 2022, the usage of #ai in tweets about exoskeletons was mainly associated with a positive sentiment. Sixth, text processing-based approaches were used to detect possibly sarcastic tweets and tweets that contained news. Finally, a comparison of positive tweets, negative tweets, neutral tweets, possibly sarcastic tweets, and tweets that contained news, in terms of different characteristic properties of these tweets are presented. The findings reveal multiple novel insights, for instance, the average number of hashtags used in tweets that contained news has considerably increased since January 2022.

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational background, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, Natural Language Processing and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of the machine learning experiments with the domain-specific textual data set, considering 2 research directions: a. predictions analysis and b. data balancing. Wrong predictions analysis and the features that contributed to misclassification, along with correct predictions analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel 4-step methodology is proposed in this paper for the first time, consisting of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtains better results than data undersampling in imbalanced data sets, while hybrid approaches perform reasonably well.

A critical aspect of coronary heart disease (CHD) care and secondary prevention is ensuring patients have access to evidence-based information. The purpose of this review is to summarise the guiding principles, content, context and timing of information and education that is beneficial for supporting people with CHD and potential communication strategies including digital interventions. We conducted a scoping review involving searching four databases (Web of Science, PubMed, CINAHL, Medline) for articles published from January 2000 to August 2022. Literature was identified through title and abstract screening by expert reviewers. Evidence was synthesised according to the review aims. Results demonstrated that information-sharing, decision-making, goal-setting, positivity and practicality are important aspects of secondary prevention and should be patient-centred and evidenced based with consideration of patient need and preference. Initiation and duration of education is highly variable between and within people, but hence communication and support should be regular and ongoing. In conclusion, text messaging programs, smartphone applications and wearable devices are examples of digital health strategies that facilitate education and support for patients with heart disease. There is no one size fits all approach that suits all patients at all stages and hence flexibility and a suite of resources and strategies is optimal.

Reading is one of the essential processes during the maturation of an individual. It is estimated that 5-10% of school-age children are affected by dyslexia, the reading disorder characterised by difficulties in the accuracy or fluency of word recognition. There are many studies which have reported that colour overlays and background could improve the reading process, especially in children with reading disorders. As dyslexia has neurobiological origins, the aim of the present research was to understand the relationship between physiological parameters and colour modifications in the text and background during reading in children with and without dyslexia. We have measured differences in electroencephalography (EEG), heart rate variability (HRV), electrodermal activities (EDA), and eye movement of the 36 school-age children (18 with dyslexia and 18 of control group) during the reading performance in 13 combinations of background and overlay colours during the reading task. Our findings showed that the dyslexic children have longer reading duration, fixation count, fixation duration average, fixation duration total, and longer saccade count, saccade duration total, and saccade duration average while reading on white and coloured background/overlay. It was found that the turquoise, turquoise O, and yellow colours are beneficial for dyslexic readers, as they achieved the shortest time duration during the reading tasks when these colours were used. Also, dyslexic children have higher values of beta and the whole range of EEG while reading in particular colour (purple), as well as increasing theta range while reading on the purple overlay colour. We have observed no significant differences between HRV parameters on white colour, except for single colours (purple, turquoise overlay and yellow overlay) where the control group showed higher values for Mean HR, while dyslexic children scored higher with Mean RR. Regarding EDA measure we have found systematically lower values in children with dyslexia in comparison to the control group. Based on present results we can conclude that both colours (warm and cold background/overlays) are beneficial for both groups of readers and all sensor modalities could be used to better understand the neurophysiological origins in dyslexic children.

Food insecurity is an economic and social condition involving limited or uncertain access to food. The problem of food insecurity in communities is influenced by economic conditions, food deserts, and barriers to accessing healthy food. Individuals experiencing food insecurity often endure concurrent problems of financial instability, hunger, and poor mental and physical health. Public and non-profit services in the U.S., such as the federally supported Supplemental Nutrition Assistance Program (SNAP) and community food banks, provide food-related assistance to individuals who are at a high risk of experiencing food insecurity. Unfortunately, many individuals who qualify for these services still experience food insecurity due to barriers preventing them from accessing food. Effective approaches for removing barriers that prevent individuals from accessing food are needed to mitigate the increased risk of chronic disease among vulnerable populations. This study tested a novel food insecurity intervention called FINDING-Food, which stands for Frequent Informational Nudges Directing Individuals to Needed Goods. Informational nudges in the intervention promoted food security through the elimination of information barriers to accessing food. The intervention used in this mixed-methods feasibility study consisted of informational nudges in the form of weekly text messages that were sent to food pantry clients experiencing food insecurity. The study aims were to test the efficacy and acceptability of the intervention by examining whether the informational nudges could enhance food pantry utilization, increase SNAP registration, and promote food security. Quantitative study results showed a lower prevalence of food insecurity in the intervention group than the control group at the end of the study. Qualitative findings revealed how the intervention group found the text messages to be helpful and informative. These study findings can enhance future food insecurity interventions aiming to eliminate barriers that prevent individuals who are food insecure from accessing healthy food.

Detecting suicidal ideation in adults with major depression is crucial for timely intervention and prevention of self-harm. As suicide is influenced by various biological, socio-cultural and psychological factors, traditional screening methods have accuracy and efficiency limitations. In certain cultures, societal stigma and marginalization can compel individuals with depression to conceal their suffering. Such individuals often turn to online social media platforms and share their experiences with peers under the protection of anonymity. Our research explored the potential of machine learning detection of suicidal ideation among Romanian adults with major depression that contributed to a web-based depression support forum. A trained algorithm (C4.5 decision tree) analyzed 125 posts fed to on a free access online support forum over 5 years (2014 – 2018) and classified them based on suicidal ideation content. 32 texts (25%) were identified as having a high probability of suicidal ideation content. 65% of the authors were male, with a mean age of 36.7±10.3 years and an average duration of illness of 3.4±1.4 years. Texts indicating positive suicidal ideation were generally shorter and elicited more general responses but fewer professional responses compared to those without suicidal ideation content. The study's main limitations include the relatively small number of classified texts, the absence of prospective information and the lack of qualitative evaluation of the excerpts' content. As socio-demographic and linguistic actuarial results were comparable to data reported by real life studies, we may consider basic text mining techniques as a screening tool that is able to detect suicidal ideation in texts written in unstructured Romanian language.

Cultural ecosystem services has been increasingly influential in both environmental research and policy decision-making, such as for urban green spaces However, its popular definition conflates the concepts of ‘services’ and ‘benefits’ which made it challenging for planners to employ it directly for urban green space management. One the most widely used definition of this non-tangible ecosystem services are “functions of environmental spaces and cultural activities which may then result in the enjoyment of cultural ecosystem benefits”; yet the latter itself have never found its way into official laws and regulations. In this study, via a case study in Singapore, we propose new evidence to re-evaluate and re-position the two of the most important emerging concepts in managing the green spaces in urban areas. Using the transdisciplinary mixed methods of public participation GIS and social media text mining analysis, a wealth of cultural ecosystem services and their associated benefits were reported. This was especially so with regards to recreational and aesthetic services and experiential benefits. Recommendations to improve the park were also suggested, alongside sharing of methodological considerations for future research. Overall, this paper recommends the employment of the redefined cultural ecosystem services conceptual framework to generate relational, data-driven and actionable insights to better support urban green space management, which is not only useful to Singapore governments but also world-wide relevant.

Efficient textual data distributions (TDD) alignment and generation are open research problems in textual analytics and NLP. It is presently difficult to parsimoniously and methodologically confirm that two or more natural language datasets belong to similar distributions, and to identify the extent to which textual data possess alignment. This study focuses on addressing a segment of the broader problem described above by applying multiple supervised and unsupervised machine learning (ML) methods to explore the behavior of TDD by (i) topical alignment, and (ii) by sentiment alignment. Furthermore we use multiple text generation methods including fine-tuned GPT-2, to generate text by topic and by sentiment. Finally we develop a unique process driven variation of Kullback-Leibler divergence (KLD) application to TDD, named KL Textual Distributions Contrasts (KL-TDC) to identify the alignment of machine generated textual corpora with naturally occurring textual corpora. This study thus identifies a unique approach for generating and validating TDD by topic and sentiment, which can be used to help address sparse data problems and other research, practice and classroom situations in need of artificially generated topic or sentiment aligned textual data.

Malaysian healthcare institutions still use ineffective paper-based vaccination systems to manage childhood immunization schedules. This may lead to missed appointments, incomplete vaccinations, and outbreaks of preventable diseases among infants. To address this issue, we proposed a text messaging vaccination reminder and recall system named Virtual Health Connect (VHC) to simplify and accelerate the immunization administration for nurses that may result in improving the completion and timeliness of immunizations among infants. Considering the narrow research about the acceptance of these systems in the healthcare sector, we examined factors influencing nurses’ attitude and intention to use VHC using the extended technology acceptance model (TAM). The novelty of the conceptual model is proposing new predictors of attitude, namely, perceived compatibility and perceived privacy and security issues. We conducted a survey among 121 nurses in Malaysian government hospitals and clinics to test the model. We analyzed the collected data using partial least squares-structural equation modeling (PLS-SEM) to examine the significant factors influencing nurses’ attitude and intention to use VHC. Moreover, we applied artificial neural network (ANN) to determine the most significant factors of acceptance with higher accuracy. Therefore, we could offer more accurate insights to decision-makers in the healthcare sector for the advancement of health services. Our results highlighted that the compatibility of VHC with the current work setting of nurses developed their positive perspectives toward the system. Moreover, the nurses felt optimistic about the system when considering it to be useful and easy to use in the workplace. Finally, their attitude toward using VHC played a pivotal role in raising their intention. Based on the ANN models, we also found that perceived compatibility was the most significant factor influencing nurses' attitude towards using VHC, followed by perceived ease of use and perceived usefulness.

Artificial Intelligence (AI) experienced significant advancements in recent years, and its impact is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential impact of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather infor-mation on GPT-3`s perceptions of AI impact on sustainable development. Analysis of GPT-3’s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We plan subsequent research for effects on ecologic and economic domains of SDGs. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.

The adoption of digital health technologies accelerated during Covid-19, with concerns over the equity of access due to digital exclusion. Using data from a text-based online mental health service for children and young people we explore the impact of the pandemic on service access and presenting concerns and whether differences were observed by sociodemographic characteristics in terms of access (gender, ethnicity and deprivation). We used interrupted time-series models to assess whether there was a change in the level and rate of service use during the Covid-19 pandemic (April 2020-April 2021) compared to pre-pandemic trends (June 2019-March 2020). Routinely collected data from 61221 service users were extracted for observation, those represented half of the service population as only those with consent to share their data were used. The majority of users identified as female (74%) and White (80%), with an age range between 13 and 20 years of age,. There was evidence of a sudden increase (13%) in service access at the start of the pandemic (RR 1.13 95% CI 1.02, 1.25), followed by a reduced rate (from 25% to 21%) of engagement during the pandemic compared to pre-pandemic trends (RR 0.97 95% CI 0.95,0.98). There was a sudden increase in almost all presenting issues apart from physical complaints. There was evidence of a step increase in the number of contacts for Black/African/Caribbean/Black British (38% increase; 95% CI: 1%-90%) and White ethnic groups (14% increase; 95% CI: 2%-27%) ), the sudden increase in service use at the start of the pandemic for the most (58% increase; 95% CI: 1%-247%) and least (47% increase; 95% CI: 6%-204%) deprived areas. During the pandemic, contact rates decreased, and referral sources change at the start. Findings on access and service activity align with other studies observing reduced service utilisation. The lack of differences in deprivation levels and ethnicity at lockdown suggests exploring equity of access to the anonymous service. The study provides unique insights into changes in digital mental health use during Covid-19 in the UK.

Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.

Artificial Intelligence (AI) has the power to generate scenarios and make predictions through the use of advanced algorithms and machine learning techniques. OpenAI’s GPT-3 AI is a state-of-the-art language model that has been trained on a large dataset of text, which allows it to generate hu-man-like text, and it can generate scenarios for different fields. However, GPT-3 was trained on data available up until June 2021, had no access to more recent data nor was connected to the internet. In this study, we investigated the capability of OpenAI’s GPT-3 AI to predict the Ukrainian war es-calation, which started in 2022 and had massive geopolitical effects. We used GPT-3´s capability to generate future scenarios, to check those scenarios for internal consistency, and to create a proba-bility estimate. The results showed that although GPT-3 described an open war as one of the low probability scenarios, its capability on predicting the future was limited. Furthermore, it became evident that checking internal consistency of the generated scenarios could be improved. We ap-preciated GPT-3 as very useful and powerful for generating future scenarios, but also concluded that its prediction capabilities of real-world events are limited, should be used with caution, and require further development.

Identifying or authenticating a computer user are necessary steps to keep systems secure on the network and to prevent fraudulent users from accessing accounts. Keystroke dynamics authentication can be used as an additional authentication method. Keystroke dynamics involves in-depth analysis of how you type on the keyboard, analysis of how long a key is pressed or the time between two consecutive keys. This field has seen a continuous growth in scientific research. In the last five years alone, about 10,000 scientific researches in this field have been published. One of the main problems facing researchers is the small number of public data sets that include how users type on the keyboard. This paper aims to provide researchers with a data set that includes how to type free text on the keyboard by 80 users. The data were collected in a single session via a web platform. The dataset contains 410,633 key-events collected in a total time interval of almost 24 hours. In similar research, most datasets are with texts written by users in English. The language in which the users wrote for this research is Romanian. This paper also provides an extensive analysis of the data set collected and presents relevant information for the analysis of the data set in future research.

Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

We have studied the very large amount of literary works written by the Italian mystic Maria Valtorta to assess similarities and differences in her writings because she claims that most of them are due to mystical visions. We have used mathematical and statistical tools developed for specifically studying deep linguistic aspects of texts. The general trend indicates that the literary works explicitly attributable to Maria Valtorta differ significantly from her other literary works, that she claims are attributable to the alleged characters Jesus and Mary. Mathematically, they seem to have been written by different authors. The comparison with the Italian literature is very striking. A single author, namely Maria Valtorta, seems to be able to write texts so diverse to cover the entire mathematical range (suitable defined) of the Italian literature of seven centuries.

For Visually impaired People (VIPs), the ability to convert text to sound can mean a new level of independence or the simple joy of a good book. With significant advances in Optical Character Recognition (OCR) in recent years, a number of reading aids are appearing on the market. These reading aids convert images captured by a camera to text which can then be read aloud. However, all of these reading aids suffer from a key issue – the user must be able to visually target the text and capture an image of sufficient quality for the OCR algorithm to function – no small task for VIPs. In this work, a Sound-Emitting Document Image Quality Assessment metric (SEDIQA) is proposed which allows the user to hear the quality of the text image and automatically captures the best image for OCR accuracy. This work also includes testing of OCR performance against image degradations, to identify the most significant contributors to accuracy reduction. The proposed No-Reference Image Quality Assessor (NR-IQA) is validated alongside established NR-IQAs and this work includes insights into the performance of these NR-IQAs on document images.