Text Databases Research Articles

Text Analytics: "Graphic Visualization in Education and a Scientometric Analysis Using R Tool to Explore the Impact and Trends in Classroom Learning."

Text analytics can be applied in education to gain insights into student learning, teacher performance, and educational trends by analysing various forms of text-based data such as student essays, social media posts, and educational resources. While the need for teacher preparation and training is identified, specific solutions or effective strategies for equipping educators with the skills and knowledge to integrate graphic visualizations into their teaching practices might be underexplored. This Scientometrics study used PRISMA approach (Preferred Reporting Items for Systematic Review and Meta-Analysis) Using R tool. Scopus databases that were relevant to the research topic, and its standardized procedures ensured comprehensiveness and replicability. The study found 85 articles using the PRISMA approach from the Scopus database, with 98.82% being articles and 1.18% from conference proceedings. The number of articles published increased in 2022, with 33 articles, compared to 22 articles in 2020. The total number of cited papers was 70, with a total citation of 969. This indicates an increase in research related to graphic visualization in education due to technological advancements. The citation per cited paper was 119. Besides, the study identifies the top 10 countries that have conducted research in graphic visualization, with the United States leading with 19 studies followed by China with 12 and Spain with 8. The top three subject areas in the current research trends are Computer Science education with 78 publications, Engineering education with 33, and Social Science education with 14. Other fields related to language and linguistics are also being given special attention. These findings suggest that education systems and research in education are keeping up with global developments. There is potential for visualizations to be integrated with AI technologies to provide personalized learning experiences, while ensuring that visualizations are accessible to all students, including those with visual impairments. Studies have shown that using these approaches in education can enhance student learning and understanding of complex concepts.

Does heterogeneous media sentiment matter the ‘green premium’? An empirical evidence from the Chinese bond market

This paper selects 346 green bonds issued in China from 2016 to 2021 as the sample, and the Propensity Score Matching (PSM) method is employed to confirm the existence of ‘green premium’ in the Chinese bond market. On this basis, data on internet media sentiment and print media sentiment are collected from ‘Sina Weibo’ and ‘China Important Newspaper Full Text Database’ by both Web Crawler Technology and Textual Analysis Methods to explore the impact and the mechanism of heterogeneous media sentiments on the ‘green premium’. The results show that both the optimism of internet media and print media can significantly promote the ‘green premium’ of green bonds, and the influence of print media sentiment on the ‘green premium’ is greater than that of internet media sentiment. In addition, the Bootstrap method verifies the mediating effect of print media sentiment in the influence of internet media sentiment on ‘green premium’, indicating that print media sentiment is an important transmission path. Moreover, the results of the heterogeneity test show that the more optimistic the media is, the more significant the ‘green premium’ effect is in the regions with higher institutional environments and financial subsidies. The ‘green premium’ of green bonds is most pronounced for higher levels of institutional environment and green bond preferential policies.

Safety and efficacy of enhanced recovery after surgery among patients undergoing percutaneous nephrolithotomy: a systematic review and meta-analysis.

The enhanced recovery after surgery (ERAS) method has been widely used in surgery and anesthesia worldwide and has been applied to a wide range of surgical specialties, including colorectal surgery, gynecology, liver surgery, breast surgery, urology, and spinal surgery. An increasing number of studies have demonstrated its safety and efficacy in various fields. The safety and effectiveness of ERAS for percutaneous nephrolithotomy (PCNL) remain controversial. This study aimed to review the safety and effectiveness of ERAS for PCNL. The Chinese National Knowledge Infrastructure (CNKI), Wan Fang, Chinese Biomedical Literature Service System (SinoMed), Chinese Science and Technology Journal Full Text Database (VIP), Cochrane Library, PubMed, Web of Science, and Embase databases were searched for eligible studies published until September 19, 2022. Outcome measures included postoperative hospital stay, total hospital stay, incidence of postoperative complications, stone-free rate (SFR), operative time, postoperative indwelling nephrostomy tube time, catheter encumbrance time, and nursing satisfaction. All analyses were performed using random effects or fixed effects models. Clinical heterogeneity was treated with subgroup, sensitivity, or descriptive analyses only when clinical heterogeneity was not excluded. Publication bias was assessed using funnel plots. Twenty-five studies (1,545 observational patients and 1,562 controls) were included. The ERAS group had a shorter postoperative hospital stay [WMD=-2.59, 95% CI=(-3.04, -2.14), P<0.001], total hospital stay [WMD=-2.59, 95% CI=(-3.04, -2.14), P<0.001], and lower complication rate [RR=0.36, 95% CI=(0.29, 0.43), P<0.001] than the control group. The ERAS group had a shorter surgery time [WMD=-3.57, 95% CI=(-5.88, -1.26), P=0.003], postoperative indwelling nephrostomy tube time [WMD=-1.94, 95% CI=(-2.69, -1.19), P<0.001], catheter encumbrance time [WMD=-2.65, 95% CI=(-4.83, -0.46), P=0.02], and higher satisfaction [RR=1.15, 95% CI=(1.05, 1.25), P=0.001] than the control group. The difference in the stone-free rate between the two groups was not statistically significant [RR=1.03, 95% CI=(0.97, 1.09), P=0.38], but the stone-free rate of the observation group (ERAS group) in each study was higher than that of the control group. ERAS not only ensures the safety of PCNL but also promotes postoperative rehabilitation of patients (shorter surgery time, postoperative indwelling nephrostomy tube time, postoperative hospital time, and lower complication rate). At the same time, differences in the stone-free rate were not statistically significant, but the stone-free rate of ERAS in each study was higher than that of the usual care for PCNL patients.

Management of chronic pain in a rural Australian setting: Findings from the Crossroads-II mixed-methods study.

A cross-sectional survey (2016-18) involving quantitativeand text-based data was completed by adultsfrom randomly selected households in the Goulburn Valley, Victoria. Among the 2680 respondents, 594 (22%) reported chronic pain. Few (6%) respondents with chronic pain were accessing specialist pain clinics. Logistic regression analysis suggested that the strongest predictors of well‑managed chronic pain were older age, the absence of depression or disability and a longer duration of chronicpain. This study found a similar prevalence of chronic pain to other, predominantly metropolitan Australian studies, withlow participation in specialist pain clinics. The results suggest that improved access to multidisciplinary pain management approaches in rural areas is warranted.

ESG2PreEM: Automated ESG grade assessment framework using pre-trained ensemble models

Incorporating environmental, social, and governance (ESG) criteria is essential for promoting sustainability in business and is considered a set of principles that can increase a firm’s value. This research proposes a strategy using text-based automated techniques to rate ESG. For autonomous classification, data were collected from the news archive LexisNexis and classified as E, S, or G based on the ESG materials provided by the Refinitiv-Sustainable Leadership Monitor, which has over 450 metrics. In addition, Bidirectional Encoder Representations from Transformers (BERT), Robustly optimized BERT approach (RoBERTa), and A Lite BERT (ALBERT) models were trained to accurately categorize preprocessed ESG documents using a voting ensemble model, and their performances were measured. The accuracy of the ensemble model utilizing BERT and ALBERT was found to be 80.79% with batch size 20. Additionally, this research validated the performance of the framework for companies included in the Dow Jones Industrial Average (DJIA) and compared it with the grade provided by Morgan Stanley Capital International (MSCI), a globally renowned ESG rating agency known for having the highest creditworthiness. This study supports the use of sophisticated natural language processing (NLP) techniques to attain important knowledge from large amounts of text-based data to improve ESG assessment criteria established by different rating agencies.

The Emergence of Artificial Intelligence in Anticipatory Urban Governance: Multi-Scalar Evidence of China’s Transition to City Brains

ABSTRACT While smart city initiatives have characterized global urbanization from the 1990s to the 2020s, nowadays a novel artificial intelligence (AI) enabled approach to urban governance is rapidly emerging, thereby shaping the governance and planning of present and future cities. This urban phenomenon can be understood theoretically through the notion of anticipatory governance, and empirically through so-called City Brain systems. This is particularly evident in China where a wide range of urban AI solutions are being experimented at different scales which this paper seeks to illustrate. First, by building a database of AI-urban policy texts associated with Chinese cities, we capture and discuss the national network of discourses surrounding urban AI. Second, we draw on empirical research conducted in Beijing to examine an existing city brain project and explain its impact on urban governance. Our study reveals the multi-scalar policy landscape of urban AI transitions in China and sheds light on the extent to which emerging AI technologies such as city brains can proactively address urban problems, thus developing an understanding of anticipatory governance in the age of urban AI. We conclude the paper by reflecting on the complex corporate-state relations embedded in the co-production of city brains, their diffusion and impact beyond China.

China: as an Emerging Superpower

The research paper aims to answer the main research question whether China can really becomethe next superpower towards the middle of 21st century through its economic performance, energypolicy, population, nuclear capacity and ideology. The hypothesis of the research paper is that in 21stcentury international system will become a bipolar with hegemony of the United States and China.With regard to the research methods, the following methods have been used: Case Study and ContentAnalysis. Case Studies are perhabs the most widely used research approach in internationalrelations (IR).In addition, to assert the main research question the Content Analysis is used. That method involvesanalyzing large amounts of text-based data. During research it was exercised books, analytical articles,and different research papers of international research institutes: like Carnegie Center, CenterStrategic and International Studies (CSIS), Belfer Center of International Affairs Harvard KennedySchool etc.Other method is the statistical method, related to the presenting the concrete statistics, for instance,concerning GDP of China, statistics of foreign trade, energy import etc.Theoretical context in this research is theory of international relations Realism. Particularly HansMorgenthau’s Realist Theory (6 principle) second principle – National Interest defined in terms ofNational Power. According to this principle nations always define and act for securing their nationalinterests by means of power. History tells us that nations have always acted on the basis of power.

Effectiveness of information and communication technology-based integrated care for older adults: a systematic review and meta-analysis.

Information and communication technology (ICT) is a key factor in advancing the implementation of integrated care for older adults in the context of an aging society and the normalization of epidemics. This systematic review aims to comprehensively evaluate the effectiveness of ICT-based integrated care for older adults to provide input for the construction of intelligent integrated care models suitable for the context of an aging population in China. A systematic review and meta-analysis were conducted using PubMed, Web of Science Core Collection, Scopus, MEDLINE, EBSCO, EMBASE, CINAHL with full text, ProQuest, and Cochrane Library databases, along with the Google Scholar search engine, for papers published between January 1, 2000, and July 25, 2022, to include randomized controlled trials and quasi-experimental studies of ICT-based integrated care for older adults. Two reviewers independently performed literature screening, quality assessment (JBI standardized critical appraisal tool), and data extraction. The results were pooled using a random effects model, and narrative synthesis was used for studies with insufficient outcome data. We included 32 studies (21 interventions) with a total of 30,200 participants (14,289 in the control group and 15,911 in the intervention group). However, the quality of the literature could be improved. The meta-analysis results showed that ICT-based integrated care significantly improved the overall perceived health status of older adults (n=3 studies, MD 1.29 (CI 0.11 to 2.46), no heterogeneity) and reduced the number of emergency department visits (n=11 studies, OR 0.46 (CI 0.25 to 0.86), high heterogeneity) but had no significant effect on improving quality of life, mobility, depression, hospital admissions and readmissions, or mortality in older adults, with a high degree of study heterogeneity. Narrative analysis showed that the overall quality of care, primary care service use, and functional status of older adults in the intervention group improved, but the cost-effectiveness was unclear. ICT-based integrated care is effective in improving health outcomes for older adults, but the quality and homogeneity of the evidence base need to be improved. Researchers should develop intelligent integrated care programs in the context of local health and care welfare provision systems for older adults, along with the preferences and priorities of the older adults.

“The Making of Strange Bedfellows”. A Shakespeare Quotation and Its Propagation of New Proverbs

Starting as a literary quotation, “Misery acquaints a man with strange bedfellows” from William Shakespeare’s romance The Tempest (1611) reached somewhat of a proverbial status over time but certainly never became a generally employed folk proverb. That was left to its shortened rephrasing “Misery makes strange bedfellows” and its subsequent proverbial variants “Misfortune makes strange bedfellows”, “Necessity makes strange bedfellows”, “Adversity makes strange bedfellows”, “Poverty makes strange bedfellows”, and the somewhat newer “Business makes strange bedfellows.” They all have become proverbs in their own right, and yet, “Politics makes strange bedfellows” wins the prize as far as its general currency and high frequency in oral and written communications since the late eighteenth century to the present time is concerned. As numerous historical references show, Shakespeare’s original text resulted in the fascinating propagation of several proverbs following the structure of “X makes strange bedfellows.” As such they have become so popular that anti-proverbs and mere allusions are based on them, easily recalling the underlying proverbs. It might well be that even more modern proverbs will be coined on this well-established pattern in due time. With ever more textual databases becoming available, earlier historical references might well be found, but the present case study has left no proverbial stone unturned for now.

Parallelizing Bible texts: Developing the database of the Unified Bible Reader online application

The Unified Bible Reader (UBR), developed by the Károli Gáspár University of the Reformed Church in Hungary, aims to facilitate the parallel reading of various Bible versions, including original language editions and antique as well as modern translations. This paper outlines the creation of a parallelized database of Bible texts through a four-step process: digitization, data cleaning, integration, and parallelization. The paper discusses the inherent challenges of the digitization phase which involves the conversion of non-digital texts to digital format, primarily using optical character recognition (OCR) technology. Data cleaning focuses on correcting OCR errors and resolving format inconsistencies. Integration, that is, importing text into a unified database, addresses issues with rare cases of text division. Parallelization involves aligning textual units across different versions, accounting for variations in verse numbering and verse boundaries. The development of software tools enabled semi-automatic and manual alignment. The application may serve as a basis for more refined text splitting and the addition of annotations to support advanced textual analysis and translation comparisons. The UBR project marks a significant step towards comprehensive, accessible parallel Bible reading and analysis.

Fortifying Textual Integrity: Evolutionary Optimization-powered Watermarking for Tampering Attack Detection in Digital Documents

Digital document helps as the lifeblood of present communication, yet their vulnerability to tampering poses major safety anxieties. Digital text watermarking is an effective mechanism to protect the reliability of text-based data in the digital. Introducing a hidden layer of accountability and safety, allows individuals and organizations to trust the written word and make sure the truth behind all the files. Watermarking model identifies the tampering attack by inspecting the embedded signature for distortions or alterations. Watermarks can able to mechanically classify and repair themselves once tampered with, improving document resilience. Watermarking acts as a powerful tool to detect tampering attacks in digital document. By embedding strong and imperceptible watermarks in document distribution or creation, alterations are recognized by specialized procedure. This study introduces an Evolutionary Optimizer-powered Watermarking for Tampering Attack Detection in Digital Document (EO-WTAD3) model. The main intention of EO-WTAD3 approach is to support textual integrity using the applications of metaheuristic optimizer algorithm based watermarking technique for detecting tampering attacks in digital document. In the EO-WTAD3 method, a digital watermarking method has been proposed for the ownership verification and document copyright protection using data mining concept. Moreover, the EO-WTAD3 technique utilizes the concepts of data mining to define appropriate characteristics from the document for embedding watermarks. Moreover, fractional gorilla troops optimization (FGTO) algorithm can be applied for the assortment of optimal situation of watermarks in the content, ensuring both imperceptibility and strong to tamper. The performance validation of the EO-WTAD3 methodology takes place employing multiple datasets. The extensive result analysis portrayed that the EO-WTAD3 system accomplishes improve solution with other existing approaches with respect distinct aspects.

Advanced Cyber Attack Detection Using Generative Adversarial Networks and NLP

A key difficulty in the ever-changing cybersecurity scene is the detection of sophisticated cyber-attacks. Because new threats are so much more sophisticated and difficult to detect, traditional tactics typically fail. A new technique to improving cyber-attack detection skills is explored in this study. It uses Generative Adversarial Networks (GANs) and Natural Language Processing (NLP). Using GANs' realistic data generation capabilities, possible attack paths are simulated, creating a strong dataset for training detection systems. At the same time, natural language processing (NLP) methods are used to decipher the mountain of textual information produced by cyberspace, including incident reports, communication patterns, and logs. Our approach is based on building a fake dataset using GANs that mimics the features of advanced cyberattacks. A detection model is then trained using this dataset. Simultaneously, we improve the detection model's capacity to spot intricate and nuanced assault patterns by processing and analysing text-based data using natural language processing approaches. We use a benchmark cybersecurity dataset to test the integrated method. The experimental findings show that our GAN-NLP based detection system outperforms existing systems, which have an average accuracy of 85.3%, by a wide margin. It achieves a recall of 93.2%, precision of 92.5%, and accuracy of 94.7%. These findings prove that GANs and NLP work well together to identify complex cyberattacks. Finally, GANs and NLP together provide a potent instrument for better cyber-attack detection. A scalable solution that can adapt to the ever-changing nature of cyber threats is offered by this integrated approach, which also increases detection accuracy and efficiency. Improving the models and investigating their use in a real-world cybersecurity setting will be the primary goals of future research.

Employing combined spatial and frequency domain image features for machine learning-based malware detection

&lt;p&gt;The ubiquitous adoption of Android devices has unfortunately brought a surge in malware threats, compromising user data, privacy concerns, and financial and device integrity, to name a few. To combat this, numerous efforts have explored automated botnet detection mechanisms, with anomaly-based approaches leveraging machine learning (ML) gaining attraction due to their signature-agnostic nature. However, the problem lies in devising accurate ML models which capture the ever evolving landscape of malwares by effectively leveraging all the possible features from Android application packages (APKs).This paper delved into this domain by proposing, implementing, and evaluating an image-based Android malware detection (AMD) framework that harnessed the power of feature hybridization. The core idea of this framework was the conversion of text-based data extracted from Android APKs into grayscale images. The novelty aspect of this work lied in the unique image feature extraction strategies and their subsequent hybridization to achieve accurate malware classification using ML models. More specifically, four distinct feature extraction methodologies, namely, Texture and histogram of oriented gradients (HOG) from spatial domain, and discrete wavelet transform (DWT) and Gabor from the frequency domain were employed to hybridize the features for improved malware identification. To this end, three image-based datasets, namely, Dex, Manifest, and Composite, derived from the information security centre of excellence (ISCX) Android Malware dataset, were leveraged to evaluate the optimal data source for botnet classification. Popular ML classifiers, including naive Bayes (NB), multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were employed for the classification task. The experimental results demonstrated the efficacy of the proposed framework, achieving a peak classification accuracy of 93.03% and recall of 97.1% for the RF classifier using the Manifest dataset and a combination of Texture and HOG features. These findings validate the proof-of-concept and provide valuable insights for researchers exploring ML/deep learning (DL) approaches in the domain of AMD.&lt;/p&gt;

Бронхіальна астма як професійне та екологічно-обумовлене захворювання: сучасні погляди на проблему згідно рекомендацій міжнародної організації праці 2022 року

Abstract. According to the International Labour Organization (ILO), the annual incidence of occupational bronchial asthma (OBA) ranges from 12 to 300 cases per million workers. Unlike in European countries, bronchial asthma in Ukraine remains an «under-recognised» occupational disease, accounting for less than 0.2 % of occupational pathology. The aim of the study: to analyse the algorithm for diagnosing and recognising bronchial asthma as an occupational disease according to the ILO 2022 recommendations, to compare its main provisions with the current one in Ukraine in order to adapt the system of registration of OBA to the requirements of the European Union because of the European integration process. Materials and methods. An analytical review of scientific publications using the abstract databases of scientific libraries PubMed, Medline and text databases of scientific publishers PubMed, Central, BMJ group and other VIP databases. The main legislative documents of Ukraine and the ILO regulating the process of recognising occupational diseases were analysed. The methods of system and content analysis were used. The results. Population risk assessments show that the development of 9–15 % of asthma cases in working population is directly related to exposure to harmful factors in the workplace. The risk of disease development is significantly increased due to air pollution with chemical and toxic substances as a result of hostilities in Ukraine. A comparative analysis of the international principles of diagnosis and recognition of bronchial asthma as an occupational disease with a similar system in Ukraine revealed significant differences: Ukrainian documents do not contain important information on the properties of various forms of chemical compounds, their toxicological profile, the minimum duration of exposure that causes an occupational disease, and the latency period. The lack of reference to these legally defined indicators affects the objectivity of diagnosis, the unification of diagnoses, which leads to a distortion of statistical indicators of occupational morbidity in Ukraine. Conclusions. The system of diagnosis and recording of occupational diseases, including OBA, should be improved according to the current ILO 2022 recommendations because of the reintegration processes of Ukraine’s accession to the European Union. Key words: occupational bronchial asthma, sensitizers, irritants.

Non-invasive brain-computer interfaces effectively improve motor function, sensory function, and activities of daily living in patients with spinal cord injury: a systematic review and meta-analysis

After spinal cord injury (SCI), severe motor, sensory, and autonomic dysfunction of the limbs below the injury level occurs, significantly reducing the patient’s ability to perform activities of daily living (ADL) and quality of life. Brain-computer interface (BCI), as a new rehabilitation intervention, can extract brain signals, bypass the diseased segments, and translate them into commands to control external devices to restore the sensory and motor functions of SCI. However, the current high-quality research evidence for BCI treatment of SCI is insufficient, so this review aims to evaluate the effectiveness and feasibility of non-invasive BCI training for improving motor, sensory, and ADL in patients with SCI. The BCI-related trial literature was retrieved from seven databases (PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, WanFang database, and China Scientific Journals Full Text Database). A total of seven studies (n = 102 patients with SCI) were included for meta-analysis using a fixed-effect model, with effect sizes reported as standardized mean differences. BCI training effectively improve motor function in patients with SCI, especially those in the subacute phase, but to no significant effect in patients with chronic phase. Moreover, the treatment effect of BCI is better when the number of interventions is less than 30 times and a single session lasting longer than 60 minutes is more effective. BCI intervention also showed a positive impact for sensory function and ADL on patients with SCI, and improved quality of life in SCI patients. As a rehabilitation method, BCI has been proven to be effective in improving the motor function of patients with SCI. This provides more choices in future rehabilitation protocols for SCI.

Extracting Sentiments from Youtube Comments Using Deep Learning and Transformers

Abstract: YouTube is the most used social media platform, and it has been the most popular website where users can post the video. The public generally does comment, like or dislike, video-sharing on a YouTube video. Comment plays a vital role in expressing opinions and mindset, and it is used as an expression of public opinion. The massive amount of comment is generated mainly on famous channels where challenges arise to analyse public opinion or behaviour regarding that particular video. This project proposes sentiment analysis on YouTube video by Natural Language Processing (NLP) technique along with Deep Learning techniques Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) models. Sentiment analysis is when comprehension, citation, and processing of text-based data is done, and it directly converts it into sentiment information. This analysis help users to get the report of their YouTube video. The output of this analysis gives the classification of sentiment analysis, as Positive, negative, or neutral.

Особливості формування та динаміки професійної захворюваності від впливу пилових, фізичних та хімічних факторів в Україні у 2011–2021 роках

Aim. On the basis of literature data, statistical data provided by the State Labour Service of Ukraine, the Fund of Social Insurance against accidents at work and occupational diseases to analyse the situation in relation to occupational morbidity in Ukraine for the period 2011–2021, identifying trends in its dynamics, peculiarities of development depending on regional distribution, economic sectors, influence of workplace related etiological factors. Materials and Methods. Analytical review of scientific publications was performed using reference databases of Pub Med, Medline scientific libraries and text databases of Pub Med, Central, BMJ group scientific publishing houses and other VIP databases. The statistical data of the State Labour Service of Ukraine, the Fund of Social Insurance against accidents at work and occupational diseases were analysed. The methods of system, comparative and content analysis were applied. Results and Conclusions. As a result of the analysis of the situation in relation to occupational morbidity in Ukraine for the period 2011–2021 trends of its dynamics, peculiarities of development in terms of regional distribution, branches of economy, influence of workplace related etiological factors and compared with international data are determined. A decrease in the number of victims of occupational diseases over 10 years was established from 8112 to 2904 (more than 3.5 times). The occupational morbidity rate per 100,000 working population in Ukraine (4.5) is almost 7 times lower than the European average (30.1). It was determined that in the structure of occupational morbidity, the first place belongs to diseases caused by occupational dust exposure, the second – to diseases of the musculoskeletal system, the third to vibration disease, and the fourth to sensorineural deafness. The highest risk of occupational pathology development is noted in the mining industry, which is associated with the influence of a complex of unfavourable workplace factors during underground mining of minerals. A particularly catastrophic situation regarding the detection of occupational diseases is observed in agriculture; poisoning with chemical substances during the last decade has almost not been registered. Statistical data on the state of occupational morbidity in Ukraine contradict the world experience of this pathology detection, which is due to the lack of proper monitoring of the state of health of workers, as well as of managerial leverages for creating and maintaining proper working conditions. Keywords: work conditions, workplace factor, occupational diseases.

Shielding Online Communities: Natural Language Processing and Machine Learning Strategies against Social Media Intimidation

With the usage of the internet and the growing prominence of communities, like social media we have witnessed a rise in cybercrime. Among these crimes one that stands out is Intimidator, which affects both people and adults alike. The increasing incidents of cyberbullying have led to consequences such as anxiety, aggression, depression and tragically even suicide. Consequently, there is now a pressing need for content regulation on social media platforms. This research focuses on developing a model of identifying text-based bullying messages and comments by categorizing them into five distinct types; Violence, Vulgar language Offensive content, sexually explicit material, and Hate Speech. The proposed approach involves utilizing Natural Language Processing (NLP) techniques with Machine Learning methods. The dataset is initially. Processed to remove information before extracting meaningful features. Finally, the model undergoes training and testing to ensure reliable results, in detecting instances of Intimidator in text-based data.

Is artificial intelligence the future of the customer experience?

Conversations around customer experience have dramatically evolved since the advent of the Internet and big data. Not only are one-dimensional brand-to-consumer communications now reason to abandon loyalty, but personalised experiences have become an integral part of competitive advantage. An organisation’s ability to derive actional insights from consumer data at scale — including, now, text-based conversation data from customer interactions — is now a driving factor in efficient use of resources and increased profitability. This paper explores the use of artificial intelligence (AI) to build customised and automated user journeys, and how it changes the dynamic of customer service, and perhaps marketing as well, in the modern era.

Text-to-speech on health monitoring bracelet for the visually impaired

Text-to-speech (TTS) is a technology that converts text into sound using a phonetization system and is especially useful to be applied to blind aids who need information in the form of sound because of their limitations. To help the visually impaired know their health conditions where the visually impaired feel limited to going out during the pandemic. For this reason, it is necessary to make an aid that can read text-based data such as body temperature, heart rate per minute, and oxygen levels into a voice that can be heard by the blind, the method used in this study is finite state automata (FSA) which is used to split Indonesian words into words according to its syllable patterns and facilitate the pronunciation process which is included in the blind aids so that it is expected to help the visually impaired to be able to find out their health condition. In this study, the test was carried out using the confusion matrix method and the results obtained were 100% accurate, 99.71% accuracy of temperature sensor, 98% accuracy of heart rate, and 95% accuracy of oxygen saturation.