AI-Powered Semantic Framework for Inclusive Web Accessibility Evaluation

The future of AI-powered robotics promises a major transformation across various industries, offering remarkable potential to automate complex processes, boost human capabilities, and redefine numerous sectors. By utilizing machine learning, computer vision, and natural language processing, AI-driven robots can interact with their surroundings, make informed decisions, and learn from their experiences, all without direct human oversight. This fusion of robotics and AI enables machines to tackle delicate tasks such as performing surgeries in healthcare, as well as advancing manufacturing and logistics operations. In the near future, AI-powered robots are expected to become increasingly autonomous, adaptable, and proficient in managing tasks within ever-changing environments. Breakthroughs in machine perception will allow robots to better comprehend and respond to the world around them, enhancing safety and effectiveness. Furthermore, advancements in human-robot collaboration may lead to robots working alongside humans in sectors like education, hospitality, and services, improving productivity and user interactions. However, the rise of AI-powered robotics also raises important ethical, legal, and social concerns, such as job loss and privacy issues. To ensure these technologies benefit society, careful integration will be essential. Ultimately, AI-powered robots are set to play a crucial role in shaping the future, transforming how we live and work. Research significance: The future of AI-driven robotics has the potential to revolutionize various industries, including healthcare, manufacturing, logistics, and agriculture. Research in this area concentrates on improving robot autonomy, decision-making, and collaboration with humans through advanced AI technologies. As robots become smarter, they will be able to carry out intricate tasks with greater accuracy, flexibility, and efficiency, boosting productivity and safety. This research also tackles issues such as ethical concerns, reliability, and human-robot emotional interaction. Progress in AI robotics promises innovative, practical, and sustainable solutions, transforming industries and enhancing overall quality of life. Methodology: The approach to studying the future of AI-driven robotics focuses on exploring how artificial intelligence integrates with robotic systems. This involves examining progress in machine learning, computer vision, and natural language processing, which allow robots to complete intricate tasks independently. Researchers investigate technological innovations such as enhanced sensors, edge computing, and human-robot interactions. They also address ethical, societal, and economic implications, such as the effects of automation, labor markets, and safety. To forecast trends, challenges, and opportunities in AI robotics, experts use case studies, simulations, and interviews, offering a well-rounded view of its future potential Alternative: AI Algorithm Performance, Energy Efficiency, Human-Robot Interaction, Hardware Advancements, Regulation and Ethics Evaluation preference: AI Algorithm Performance, Energy Efficiency, Human-Robot Interaction, Hardware Advancements, Regulation and Ethics Results: Hardware advancements are rising to the top, while regulation and ethics are being pushed to the bottom

A comprehensive health service evaluation and monitoring framework

We are living in unprecedented times and anyone in this world could be impacted by natural disasters in some way or the other. Life is unpredictable and what is to come is unforeseeable. Nobody knows what the very next moment will hold, maybe it could be a disastrous one too. The past cannot be changed but it can act constructively towards the betterment of the current situation, ‘Precaution is better than cure’. To be above this uncertain dilemma of life and death situations, ‘Automated Identification of Disaster News for Crisis Management is proposed using Machine Learning and Natural Language Processing’. A software solution that can help disaster management websites to dynamically show the disaster relevant news which can be shared to other social media handles through their sites. The objective here is to automatically scrape news from English news websites and identify disaster relevant news using natural language processing techniques and machine learning concepts, which can further be dynamically displayed on the crisis management websites. The complete model is automated and requires no manual labor at all. The architecture is based on Machine Learning principles that classifies news scraped from top news websites using a spider-scraper into two categories, one being disaster relevant news and other being disaster irrelevant news and eventually displaying the relevant disaster news on the crisis management website.

As technological progress and population growth continue to drive rising energy demand, renewable energy has emerged as a key focus of the global energy transition due to its environmental sustainability. However, in suitability assessments and site selection for green energy projects such as photovoltaic (PV) power generation, key criteria such as supply–demand balance and land price are often inadequately considered, despite their direct impact on decision outcomes. Moreover, excessive reliance on expert judgment for weighting, along with the neglect of inter-criterion relationships, introduces uncertainty. Combined with the presence of ill-posed problems, these issues limit the practical value of the evaluation results. This study integrates economic cost–benefit analysis into the evaluation criteria system alongside climatic and geographical criteria, constructing a set of 11 spatial indicators, including global horizontal irradiation (GHI), land prices, and regional power demand, to support PV site selection. Furthermore, a comprehensive evaluation framework is proposed that combines geographic information systems (GIS), multi-criteria decision analysis (MCDA), fuzzy comprehensive evaluation (FCE), and machine learning (ML). The framework enables the collaborative optimization of expert-constrained and data-driven criteria weighting. A national suitability zoning map for PV power plants was developed and validated against actual construction cases. The results demonstrate that the proposed methodology outperforms traditional approaches, achieving a 0.1178 improvement in weight determination compared to expert-based methods, producing a photovoltaic suitability map that more accurately reflects actual construction trends, thereby providing better and more effective support for PV site planning.

A comprehensive evaluation framework that can assess a wide range of water supply and demand management policy options in terms of economic, social, environmental, risk-based, and functional performance is crucial to ascertain their level of sustainability. However, such a detailed, generic, and holistic policy evaluation framework is not found in the literature. This paper reviews studies to evaluate water supply and/or demand management options conducted during 2000–2016. Primarily, the paper reviews the evaluation criteria used by different studies for decision making given their significant difference and the importance of a comprehensive set of criteria to complete a rigorous evaluation. In addition, a comprehensive set of water supply and demand management options are not considered together for a comparative assessment to prioritise best options for a certain area and time. Further, performance of these options needs to be evaluated for a range of uncertainties arising from changes of spatial and temporal variables of the system. While this paper highlights the important aspects that need to be included in a comprehensive policy evaluation framework, available studies collectively present a rich set of information to support it.

Cyberbullying has become a ubiquitous menace in our digitally connected society, requiring strong detection and classification systems. This study presents a multi-tiered system that reliably detects and classifies instances of cyberbullying on a variety of platforms by utilising cutting-edge machine learning and natural language processing approaches. Our algorithm, which was trained on a wide range of datasets, shows excellent accuracy in differentiating between instances of cyberbullying and non-bullying situations while taking linguistic and cultural quirks into account. Furthermore, our flexible system guarantees applicability by adjusting to changing cyberbullying patterns. By promoting safer and more inclusive digital communities, our research helps to design proactive treatments that lessen the effects of online harassment. This study introduces a robust multi-tiered system designed for the detection and classification of cyberbullying across diverse digital platforms. Leveraging state-of-the-art machine learning and natural language processing techniques, our algorithm, trained on extensive datasets, exhibits exceptional accuracy in distinguishing cyberbullying instances from non-bullying scenarios while accommodating linguistic and cultural nuances. The system’s adaptability to evolving cyberbullying patterns ensures continued efficacy. By fostering safer and more inclusive online environments, our research contributes to proactive measures and mitigates the impact of digital harassment.

BackgroundIdentifying patients at high risk of stroke-associated pneumonia (SAP) may permit targeting potential interventions to reduce its incidence. We aimed to explore the functionality of machine learning (ML) and natural language processing techniques on structured data and unstructured clinical text to predict SAP by comparing it to conventional risk scores.MethodsLinked data between a hospital stroke registry and a deidentified research-based database including electronic health records and administrative claims data was used. Natural language processing was applied to extract textual features from clinical notes. The random forest algorithm was used to build ML models. The predictive performance of ML models was compared with the A2DS2, ISAN, PNA, and ACDD4 scores using the area under the receiver operating characteristic curve (AUC).ResultsAmong 5,913 acute stroke patients hospitalized between Oct 2010 and Sep 2021, 450 (7.6%) developed SAP within the first 7 days after stroke onset. The ML model based on both textual features and structured variables had the highest AUC [0.840, 95% confidence interval (CI) 0.806–0.875], significantly higher than those of the ML model based on structured variables alone (0.828, 95% CI 0.793–0.863, P = 0.040), ACDD4 (0.807, 95% CI 0.766–0.849, P = 0.041), A2DS2 (0.803, 95% CI 0.762–0.845, P = 0.013), ISAN (0.795, 95% CI 0.752–0.837, P = 0.009), and PNA (0.778, 95% CI 0.735–0.822, P < 0.001). All models demonstrated adequate calibration except for the A2DS2 score.ConclusionsThe ML model based on both textural features and structured variables performed better than conventional risk scores in predicting SAP. The workflow used to generate ML prediction models can be disseminated for local adaptation by individual healthcare organizations.

Web accessibility evaluations are typically done by means of automatic tools and by humans' assessments. Metrics about accessibility are devoted to quantify accessibility level or accessibility barriers, providing numerical synthesis from such evaluations. It is worth noting that, while automatic tools usually return binary values (meant as the presence or the absence of an error), human assessment in manual evaluations are subjective and can get values from a continuous range.

BackgroundMachine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.ObjectiveThe primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practiceMethodsThis systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.ResultsA total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.ConclusionsMachine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

The deployment of data mining into computer-assisted language learning (CALL) assessment can help to transform language learning assessment and evaluation in a way it has never been. Advanced data analysis methods managed by machine learning and natural language processing can allow teachers and educators to view and analyze their language learning process data, making it possible to see many linguistic insights. The points of the theorem have a better vision that would be not only the learners’ relationships with the digital education platforms but also the recognized value of the processed materials. Both teachers and learners can use text data in the analysis process to gain a macro view while the micro level of understanding is covered in many aspects. They help to determine and locate the levels of the language learning process that can act as a basis on which personalized feedback can be provided, with the students’ different needs in mind. Furthermore, the data-driven CALL assessment as a means allows for the improvement of the language learning tools in terms of accuracy and equips instructors with tools to do their job better by improving their different teaching strategies. Using data mining in language assessment with technology in language learning facilitates teachers and educators to develop assessment training courses that can be used in various educational institutions worldwide and eventually leads students to an active learning process.

The paper presents a comprehensive Natural Language Processing (NLP) evaluation framework for online public opinion analysis, addressing the limitations of singular NLP techniques. It highlights the significance of public opinion analysis in the era of big data and reviews commonly employed NLP methods. The proposed framework offers universality, comprehensiveness, and flexibility, aiding managers and users in better understanding product sentiments and purchasing behaviors for informed decision-making. The proposed NLP evaluation framework includes five stages, Data Collection, Qualitative Analysis, Sentiment Analysis, Semantic Relationship Mining, and Comprehensive Analysis. It involves techniques like Sentiment Analysis, Topic Modeling, and Co-Occurrence Networks to analyze sentiment and identify semantic relationships. Our case study on nursing care food market validates the framework's feasibility, proposing enhancements in taste, gifting nature, pricing, and product strategy. Future research will expand its applicability across domains and refine inter-stage connections while maintaining flexibility.

Recently, Machine Learning has been increasingly applied in journalism in different aspects like automate fact checking, discover hidden facts, and automate workflows. In this paper, we utilize Machine Learning and Natural Language Processing to develop a binary classification model that detects news agency's linguistic style in Arabic. During training phase, we explored different features, it is found that term frequency-inverse document frequency (TF-IDF) correlates more with agency's ideology. The average accuracy of our model was 90%.

ObjectivesTraditionally, summarization of research themes and trends within a given discipline was accomplished by manual review of scientific works in the field. However, with the ushering in of the age of “big data,” new methods for discovery of such information become necessary as traditional techniques become increasingly difficult to apply due to the exponential growth of document repositories. Our objectives are to develop a pipeline for unsupervised theme extraction and summarization of thematic trends in document repositories, and to test it by applying it to a specific domain.MethodsTo that end, we detail a pipeline, which utilizes machine learning and natural language processing for unsupervised theme extraction, and a novel method for summarization of thematic trends, and network mapping for visualization of thematic relations. We then apply this pipeline to a collection of anesthesiology abstracts.ResultsWe demonstrate how this pipeline enables discovery of major themes and temporal trends in anesthesiology research and facilitates document classification and corpus exploration.DiscussionThe relation of prevalent topics and extracted trends to recent events in both anesthesiology, and healthcare in general, demonstrates the pipeline’s utility. Furthermore, the agreement between the unsupervised thematic grouping and human-assigned classification validates the pipeline’s accuracy and demonstrates another potential use.ConclusionThe described pipeline enables summarization and exploration of large document repositories, facilitates classification, aids in trend identification. A more robust and user-friendly interface will facilitate the expansion of this methodology to other domains. This will be the focus of future work for our group.

The increasing number of untruths on social media has become a critical concern, affecting public sentiment and confidence. The broad spread of misleading information on the Internet and other social platforms presents a substantial barrier, exerting an impact on public sentiment, influencing political discussions, and eroding the reliability of information sources. Identifying false information on the X platform, previously known as Twitter, is an intricate task because of the network's attributes, such as conciseness, swift spread, and varied user engagements. Extracting crucial information from brief texts, such as tweets, is challenging, even with precise labeling. This study focuses on recognizing misinformation on social media platforms. The CIC Truth-Seeker Dataset 2023, one of the most extensive datasets in its category, contains over 134,000 labeled tweets. The study introduces novel methods in the field of short text classification, incorporating machine learning and natural language processing techniques (NLP). These techniques involve feature extraction using the term frequency-inverse document frequency (TF-IDF) algorithm after the dataset is preprocessed. The study then tests a number of machine learning models, including Random Forest RF, K-Nearest Neighbor KNN, Decision Tree DT, Logistic Regression LR, Naive Bayes NB, and stochastic gradient descent SGD, to see which ones can tell the most accurate difference between real and fake tweets. The findings demonstrated significant advancements in models designed to handle short text effectively, effectively addressing a practical issue such as automatically identifying fake content on social media platforms. Furthermore, we have achieved a significant advantage over previous research on the same dataset. When implementing the models on the news data, the random forest method attained the utmost accuracy at 93%, while the K-Nearest Neighbor strategy yielded a lower accuracy of 68%. This research paper aims to offer helpful information and practical answers to recognizing and reducing false news on social media platforms, specifically focusing on the X platform. Through a Truth-Seeker dataset, we will utilize machine learning methods to enhance previous text classification models.

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations.