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Engagement has been defined as an attitude toward work, as a positive, satisfying, work‐related state of mind characterized by high levels of vigour, dedication, and absorption. Both its definition and its assessment have been controversial; however, new methods for its assessment, including artificial intelligence (AI), have been introduced in recent years. Therefore, this research aims to determine the state of the art of AI in the study of engagement. To this end, we conducted a systematic review in accordance with PRISMA to analyse the publications to date on the use of AI for the analysis of engagement. The search, carried out in six databases, was filtered, and 15 papers were finally analysed. The results show that AI has been used mainly to assess and predict engagement levels, as well as to understand the relationships between engagement and other variables. The most commonly used AI techniques are machine learning (ML) and natural language processing (NLP), and all publications use structured and unstructured data, mainly from self‐report instruments, social networks, and datasets. The accuracy of the models varies from 22% to 87%, and its main benefit has been to help both managers and HR staff understand employee engagement, although it has also contributed to research. Most of the articles have been published since 2015, and the geography has been global, with publications predominantly in India and the US. In conclusion, this study highlights the state of the art in AI for the study of engagement and concludes that the number of publications is increasing, indicating that this is possibly a new field or area of research in which important advances can be made in the study of engagement through new and novel techniques. [ABSTRACT FROM AUTHOR]

The most recent and precise biological and healthcare knowledge is critical in the current outbreak such as COVID. In today's small world, everyone needs timely and appropriate medical information to prevent contagious diseases. Extraction of important information from medical conversations and dissemination to patients and doctors may benefit in the treatments of doctor tiredness and patient amnesia. Problem: Automatic text summarizing is essential for gaining great knowledge in any topic in an efficient and productive manner. The material included in health records is vital to our understanding of kind illness and its manifestations. Creating a comprehensive and standard kind of content is becoming an unavoidable and crucial problem in the medical process as a result of the massive amounts of fragmented data created in many sectors. Approach: The purpose of this study is to employ NLP‐based deep learning algorithms for text summary that perform well on linguistic text summarization data, and then modify/adapt these for biomedical domain‐specific text summarization. This paper provides an approach developed in‐house for condensing ill‐punctuated or unpunctuated discussion transcripts into more intelligible summaries, which combines topic modelling and phrase selection with punctuation restorations. For autonomous synthesis of medical reports from biomedical transcripts, this research proposes using an end‐to‐end summarization technique, Deep Dense Long Short Term Memory Network (LSTM), followed by Convolutional Neural Network (CNN). Results: Extensive testing, examination, and comparing have demonstrated that this summarizer works well for medical transcript summarization. The suggested approach achieved an average ROUGE score of 93.5% using a single document summary. Furthermore, by comparing new techniques to previous ones, the utility and accuracy of novel strategies would be shown. The results reveal that models trained on ordinary language provide comparable results on a biomedical testing set, with one model outperforming the linguistic test set. [ABSTRACT FROM AUTHOR]

The leading intention of the current paper is to review the research work accomplished by various researchers to achieve sentiment analysis on the text and to elaborate on natural language processing (NLP) and various machine learning algorithms used to evaluate textual sentiments. In this study, primitive cases are considered that used crucial algorithms, and knowledge that can be opted for sentiment analysis. A survey of the work that has been done till now is conducted observing the results and outcomes concerning varying parameters of various researchers who worked on previously existing as well as novel and hybrid algorithms opting legion methodologies. The fundamental algorithms like Support Vector Machine (SVM), Bayesian Networks (BN), Maximum Entropy (MaxEnt), Conditional Random Fields (CRF) and Artificial Neural Networks (ANN) are also discussed to achieve practice percentage and accuracy score in the field of NLP, sentiment analysis and text analytics. Various other novel approaches and algorithms like CNN, LSTM, KNN, K*, K‐means, K‐means++, SOM and ENORA, along with their limitations and the performance metrics providing accuracies for major open data sets are also analyzed. [ABSTRACT FROM AUTHOR]

Data on infrastructure project costs are often unstructured and lack consistency. To enable costs to be compared within and between organizations, large amounts of data must be classified to a common standard, typically a manual process. This is time‐consuming, error‐prone, inconsistent, and subjective, as it is based on human judgment. This paper describes a novel approach for automating the process by harnessing natural language processing identifying the relevant keywords in the text descriptions and implementing machine learning classifiers to emulate the expert's knowledge. The task was to identify "extra over" cost items, conversion factors, and to recognize the correct work breakdown structure (WBS) category. The results show that 94% of the "extra over" cases were correctly classified, and 90% of cases that needed conversion, correctly predicting an associated conversion factor with 87% accuracy. Finally, the WBS categories were identified with 72% accuracy. The approach has the potential to provide a step change in the speed and accuracy of structuring and classifying infrastructure cost data for benchmarking. [ABSTRACT FROM AUTHOR]

Despite improved ancillary investigations in epilepsy care, patients' narratives remain indispensable for diagnosing and treatment monitoring. This wealth of information is typically stored in electronic health records and accumulated in medical journals in an unstructured manner, thereby restricting complete utilization in clinical decision‐making. To this end, clinical researchers increasing apply natural language processing (NLP)—a branch of artificial intelligence—as it removes ambiguity, derives context, and imbues standardized meaning from free‐narrative clinical texts. This systematic review presents an overview of the current NLP applications in epilepsy and discusses the opportunities and drawbacks of NLP alongside its future implications. We searched the PubMed and Embase databases with a "natural language processing" and "epilepsy" query (March 4, 2022) and included original research articles describing the application of NLP techniques for textual analysis in epilepsy. Twenty‐six studies were included. Fifty‐eight percent of these studies used NLP to classify clinical records into predefined categories, improving patient identification and treatment decisions. Other applications of NLP had structured clinical information retrieval from electronic health records, scientific papers, and online posts of patients. Challenges and opportunities of NLP applications for enhancing epilepsy care and research are discussed. The field could further benefit from NLP by replicating successes in other health care domains, such as NLP‐aided quality evaluation for clinical decision‐making, outcome prediction, and clinical record summarization. [ABSTRACT FROM AUTHOR]

The article offers information on the increasing popularity of ChatGPT and other chatbots worldwide, particularly in education and research fields.

Advances in Internet of Things (IoT) and analytic‐based systems in the past decade have found several applications in medical informatics, and have significantly facilitated healthcare decision making. Patients' data are collected through a variety of means, including IoT sensory systems, and require efficient, and accurate processing. Topic Modelling is an unsupervised machine learning algorithm for Natural Language Processing (NLP) that identifies relationships and associations within textual data. The application of Topic Modelling has been widely used on raw text data, where meaningful clusters (topics) are generated by the model. The purpose of this paper is to explore the varying methods of Topic Modelling, mostly the Latent Dirichlet allocation (LDA) model, and its applicability on personalized diabetes management. The proposed study evaluates the possibility of applying topic modelling methods on diabetes literature and genomic data in order to achieve precision medicine. [ABSTRACT FROM AUTHOR]

With the surge of searching and reading online health‐based articles, maintaining the quality and credibility of online health‐based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate the assessment and scoring of online health‐related articles' credibility. The paper proposed an Attention‐based Recurrent Multichannel Convolutional Neural Network (ARMCNN) model. The proposed model incorporates a BiLSTM layer, a multichannel CNN layer, and an attention layer and predicts the credibility of online health information. To perform a reliable evaluation of the presented model, we utilize the health articles reviewed by the experts, compiled in a labeled dataset termed "Pubhealth," which consists of thousands of health articles. The results are evaluated using five performance measures, accuracy, precision, recall, f1‐score, and area under the ROC curve (AUC). Furthermore, we extensively compared the proposed model with different deep learning and machine learning models such as Long short‐term memory (LSTM), Bidirectional LSTM, CNN (Convolutional neural network), and RNN‐CNN. The experimental results showed that the proposed model produced state‐of‐the‐art performance on the used dataset by achieving an accuracy of 0.88, precision of 0.92, recall of 0.87, f1‐score of 0.90, and AUC of 0.94. Further, the proposed model yielded better performance than other benchmarked techniques for the credibility assessment of online health articles. [ABSTRACT FROM AUTHOR]

Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI‐powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets. [ABSTRACT FROM AUTHOR]

Sequence to sequence models (seq2seq) require a large amount of labelled training data to learn the mapping between the input and output. A large set of misspelled words together with their corrections is needed to train a seq2seq spelling correction system. Low‐resource languages such as Turkish usually lack such large annotated datasets. Although misspelling‐reference pairs can be synthesized with a random procedure, the generated dataset may not well match to genuine human‐made misspellings. This might degrade the performance in realistic test scenarios. In this paper, we propose a novel procedure to automatically introduce human‐like misspellings to legitimate words in Turkish language. Generated human‐like misspellings are used to improve the performance of a seq2seq spelling correction system. The proposed system consists of two separate models; a misspelling generator and a spelling corrector. The generator is trained using a relatively small number of human‐made misspellings and their manual corrections. Reference words and their misspellings are used as inputs and outputs of the generator, respectively. As a result, it is trained to add realistic spelling errors to the valid words. Training data of the spelling corrector is augmented by the generator's human‐like misspellings. In the experiments, we observe that the data augmentation significantly improves the spelling correction performance. Our proposed method yields 5% absolute improvement over the state‐of‐the‐art Turkish spelling correction systems in a test set which contains human‐made misspellings from Twitter messages. [ABSTRACT FROM AUTHOR]

Writing style is considered the manner in which an author expresses his thoughts, influenced by language characteristics, period, school, or nation. Often, this writing style can identify the author. One of the most famous examples comes from 1914 in Portuguese literature. With Fernando Pessoa and his heteronyms Alberto Caeiro, Álvaro de Campos, and Ricardo Reis, who had completely different writing styles, led people to believe that they were different individuals. Currently, the discussion of authorship identification is more relevant because of the considerable amount of widespread fake news in social media, in which it is hard to identify who authored a text and even a simple quote can impact the public image of an author, especially if these texts or quotes are from politicians. This paper presents a process to analyse the emotion contained in social media messages such as Facebook to identify the author's emotional profile and use it to improve the ability to predict the author of the message. Using preprocessing techniques, lexicon‐based approaches, and machine learning, we achieved an authorship identification improvement of approximately 5% in the whole dataset and more than 50% in specific authors when considering the emotional profile on the writing style, thus increasing the ability to identify the author of a text by considering only the author's emotional profile, previously detected from prior texts. [ABSTRACT FROM AUTHOR]

Tsetlin Machines (TM) use finite state machines for learning and propositional logic to represent patterns. The resulting pattern recognition approach captures information in the form of conjunctive clauses, thus facilitating human interpretation. In this work, we propose a TM‐based approach to three common natural language processing (NLP) tasks, namely, sentiment analysis, semantic relation categorization and identifying entities in multi‐turn dialogues. By performing frequent itemset mining on the TM‐produced patterns, we show that we can obtain a global and a local interpretation of the learning, one that mimics existing rule‐sets or lexicons. Further, we also establish that our TM based approach does not compromise on accuracy in the quest for interpretability, via comparison with some widely used machine learning techniques. Finally, we introduce the idea of a relational TM, which uses a logic‐based framework to further extend the interpretability. [ABSTRACT FROM AUTHOR]

Numerical weather prediction has traditionally been based on the models that discretize the dynamical and physical equations of the atmosphere. Recently, however, the rise of deep learning has created increased interest in purely data‐driven medium‐range weather forecasting with first studies exploring the feasibility of such an approach. To accelerate progress in this area, the WeatherBench benchmark challenge was defined. Here, we train a deep residual convolutional neural network (Resnet) to predict geopotential, temperature and precipitation at 5.625° resolution up to 5 days ahead. To avoid overfitting and improve forecast skill, we pretrain the model using historical climate model output before fine‐tuning on reanalysis data. The resulting forecasts outperform previous submissions to WeatherBench and are comparable in skill to a physical baseline at similar resolution. We also analyze how the neural network creates its predictions and find that, for the case studies analyzed, the model has learned physically reasonable correlations. Finally, we perform scaling experiments to estimate the potential skill of data‐driven approaches at higher resolutions. Plain Language Summary: Weather forecasts are created by running hugely complex computer simulations that encapsulate our knowledge of how the atmosphere works. This approach has served us well but is there a different way? The paradigm of machine learning proposes learning an algorithm from data rather than building it from physical principles. For several areas like computer vision and natural language processing this has worked exceedingly well, so it just makes sense to try it as well for weather forecasting. This paper presents the latest attempt at training a machine learning weather forecasting model. It is shown that the learned model produces reasonable forecasts, approximately on par with traditional models run on much lower resolution. However, there is still a large gap to current state–of–the–art high–resolution weather models that is unlikely to be closed with a purely data–driven approach because not enough training data exists. Key Points: A large convolutional neural network is trained for the WeatherBench challengePretraining on climate model data improves skill and prevents overfittingThe model sets a new state‐of‐the‐art for data‐driven medium‐range forecasting [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state‐of‐the‐art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short‐term memory and back propagation through time approaches. The proposed models consider both language‐dependent features, such as part‐of‐speech tags, and language‐independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f‐measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively. [ABSTRACT FROM AUTHOR]

We study the problem of entity salience by proposing the design and implementation of Swat, a system that identifies the salient Wikipedia entities occurring in an input document. Swat consists of several modules that are able to detect and classify on‐the‐fly Wikipedia entities as salient or not, based on a large number of syntactic, semantic, and latent features properly extracted via a supervised process, which has been trained over millions of examples drawn from the New York Times corpus. The validation process is performed through a large experimental assessment, eventually showing that Swat improves known solutions over all publicly available datasets. We release Swat via an API that we describe and comment in the paper to ease its use in other software. [ABSTRACT FROM AUTHOR]

Across Europe, negative public opinion has and may continue to limit the deployment of renewable energy infrastructure required for the transition to net‐zero energy systems. Understanding public sentiment and its spatio‐temporal variations is as such important for decision‐making and for developing socially accepted energy systems. In this study, we apply a sentiment classification model based on a machine learning framework for natural language processing, NorBERT, on data collected from Twitter between 2006 and 2022 to analyse the case of wind power opposition in Norway. From the 68,828 tweets with geospatial information, we show how discussions about wind power intensified in 2018/2019 together with a trend of more negative tweets up until 2020, both on a regional level and for Norway as a whole. Furthermore, we find weak geographical clustering in our data, indicating that discussions are country wide and not dominated by specific regional events or developments. Twitter data allow for detailed insight into the temporal nature of public sentiments and extending this research to additional case studies of technologies, countries and sources of data (e.g. newspapers, other social media) may prove important to complement traditional survey research and the understanding of public sentiment. [ABSTRACT FROM AUTHOR]

A variety of detailed data about geological topics and geoscience knowledge are buried in the geoscience literature and rarely used. Named entity recognition (NER) provides both opportunities and challenges to leverage this wealth of data in the geoscience literature for data analysis and further information extraction. Existing NER models and techniques are mainly based on rule‐based and supervised approaches, and developing such systems requires a costly manual effort. In this paper, we first design a generic stepwise framework for domain‐specific NER. Following this framework, domain‐specific entities and domain‐general words are collected and selected as seed terms. Normalization and grouping processes are then applied to these seed terms for further analysis. A random extraction algorithm based on a unigram language model is used to generate a large‐scale training data set consisting of probabilistically labeled pseudosentences. Each generated sentence is then used as input to the self‐training and learning algorithm. Experimental results on two constructed data sets demonstrate that the proposed model effectively recognizes and identifies geological named entities. Plain Language Summary: Existing entity recognition and classification methods are less functional for automatic geological name entity recognition. In this paper, we propose a stepwise unsupervised approach to geological name entity recognition in geological reports that requires no labeled data. This approach dynamically adapts to extract and identify unseen instances. We hope that our approach will serve as an alternative method that deserves further study. Key Points: Geological named entities are extracted from unstructured Chinese geoscience reports using deep learningThe proposed framework can be easily extended to other subject domains through fine‐tuningA training data set is constructed based on both domain‐specific entities and domain‐general words [ABSTRACT FROM AUTHOR]

Abstract: Natural language processing is a composition of several error‐prone and challenging tasks, including part of speech tagging, word sense disambiguation, named entity recognition, and compound verb detection. Studying intrasentence relations and roles is essential to improve the mentioned subtasks. Semi‐automatic schemes such as ontologies can be applied to clarify word's dependencies. This paper presents an ontology that is targeting to improve POS tagging, WSD, NER, and compound verb detection in Persian with extra properties that may ameliorate machine translation. The ontology is tested in combinations with several state‐of‐art algorithms on Dadegan corpus. The results show that coping semantic analysis with machine learning methods enhance relation detection and consequently precision of the mentioned subtasks, which is not widely addressed in Persian. Furthermore, the experimental results declare that the accuracy rate increases between 4.5 and 23% for different tasks. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to analyze publications on artificial intelligence (AI) for falls in older people from a bibliometric perspective. Methods: The Web of Science database was searched for titles of English‐language articles containing the words "artificial intelligence," "deep learning," "machine learning," "natural language processing,", "neural artificial network," "fall," "geriatric," "elderly," "aging," "older," and "old age." An R‐based application (Biblioshiny for bibliometrics) and VOSviewer software were used for analysis. Results: Thirty‐seven English articles published between 2018 and 2024 were included. The year 2023 is the year with the most publications with 16 articles. The most productive research field was "Engineering Electrical Electronic" with seven articles. The most productive country was the United States, followed by China. The most common words were "injuries," "people," and "risk factors." Conclusion: Publications on AI and falls in the elderly are both few in number and the number of publications has increased in recent years. Future research should include relevant analyses in scientific databases, such as Scopus and PubMed. [ABSTRACT FROM AUTHOR]

Aim: Depressive disorder is often evaluated using established rating scales. However, consistent data collection with these scales requires trained professionals. In the present study, the "rater & estimation‐system" reliability was assessed between consensus evaluation by trained psychiatrists and the estimation by 2 models of the AI‐MADRS (Montgomery‐Asberg Depression Rating Scale) estimation system, a machine learning algorithm‐based model developed to assess the severity of depression. Methods: During interviews with trained psychiatrists and the AI‐MADRS estimation system, patients responded orally to machine‐generated voice prompts from the AI‐MADRS structured interview questions. The severity scores estimated from two models of the AI‐MADRS estimation system, the max estimation model and the average estimation model, were compared with those by trained psychiatrists. Results: A total of 51 evaluation interviews conducted on 30 patients were analyzed. Pearson's correlation coefficient with the scores evaluated by trained psychiatrists was 0.76 (95% confidence interval 0.62–0.86) for the max estimation model, and 0.86 (0.76–0.92) for the average estimation model. The ANOVA ICC rater & estimation‐system reliability with the evaluation scores by trained psychiatrists was 0.51 (−0.09 to 0.79) for the max estimation model, and 0.75 (0.55–0.86) for the average estimation model. Conclusion: The average estimation model of AI‐MADRS demonstrated substantially acceptable rater & estimation‐system reliability with trained psychiatrists. Accumulating a broader training dataset and the refinement of AI‐MADRS interviews are expected to improve the performance of AI‐MADRS. Our findings suggest that AI technologies can significantly modernize and potentially revolutionize the realm of depression assessments. [ABSTRACT FROM AUTHOR]

Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation, such as knowledge graphs. Since all nodes are connected directly, node global representation encoding enables direct communication between two distant nodes while disregarding graph topology. Node local representation encoding, which captures the graph structure, considers the connections between nearby nodes but misses out onlong‐range relations. A quantum‐like approach to learning better‐contextualised node embeddings is proposed using a fusion model that combines both encoding strategies. Our methods significantly improve on two graph‐to‐text datasets compared to state‐of‐the‐art models in various experiments. [ABSTRACT FROM AUTHOR]

Premise: Quantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets. Methods: LeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 species; it employs a set of plant component detection and segmentation algorithms to isolate individual leaves, petioles, fruits, flowers, wood samples, buds, and roots. Our landmarking network automatically identifies and measures nine pseudo‐landmarks that occur on most broadleaf taxa. Text labels and barcodes are automatically identified by an archival component detector and are prepared for optical character recognition methods or natural language processing algorithms. Results: LeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel‐to‐metric conversion factors for 26 commonly used ruler types. Discussion: LeafMachine2 is a highly efficient tool for generating large quantities of plant trait data, even from occluded or overlapping leaves, field images, and non‐archival data sets. Our project, along with similar initiatives, has made significant progress in removing the bottleneck in plant trait data acquisition from herbarium specimens and shifted the focus toward the crucial task of data revision and quality control. [ABSTRACT FROM AUTHOR]

Short text classification has been a fundamental task in natural language processing, which benefits various applications, such as sentiment analysis, news tagging, and intent recommendation. However, classifying short texts is challenging due to the information sparsity in the text corpus. Besides, the performance of existing machine learning classification models largely relies on sufficient training data, yet labels can be scarce and expensive to obtain in real‐world text classification scenarios. In this article, we propose a novel self‐supervised short text classification method. Specifically, we first model the short text corpus as a heterogeneous graph to address the information sparsity problem. Then, we introduce a self‐attention‐based heterogeneous graph neural network model to learn short text embeddings. In addition, we adopt a self‐supervised learning framework to exploit internal and external similarities among short texts. Experiments on five real‐world short text benchmarks validate the effectiveness of our proposed method compared with the state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Social media has been embraced by different people as a convenient and official medium of communication. People write or share messages and attach images and videos on Twitter, Facebook and other social media platforms. It therefore generates a lot of data that is rich in sentiments. Sentiment analysis has been used to determine the opinions of clients, for instance, relating to a particular product or company. Lexicon and machine learning approaches are the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is, however, distorted by noise, the curse of dimensionality, the data domains and the size of data used for training and testing. This article aims at developing a model for sentiment analysis of social media data in which dimensionality reduction and natural language processing with part of speech tagging are incorporated. The model is tested using Naïve Bayes, support vector machine, and K‐nearest neighbor algorithms, and its performance compared with that of two other sentiment analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques. [ABSTRACT FROM AUTHOR]

Machine learning models using seismic emissions as input can predict instantaneous fault characteristics such as displacement and friction in laboratory experiments, and slow slip in Earth. Here, we address whether the seismic/acoustic emission (AE) from laboratory experiments contains information about future frictional behavior. The approach uses a convolutional encoder‐decoder containing a transformer model in the latent space, similar to models used for natural language processing. We test the model limits using progressively larger AE input time windows and progressively larger output friction time windows. The results demonstrate that very near‐term friction predictions are indeed contained in the AE signal, and predictions are progressively worse farther into the future. The future predictions by the model of impending failure in the near‐term are remarkably robust. This first effort predicting future fault frictional behavior with machine learning will aid in guiding efforts for applications in Earth. Plain Language Summary: Over the last 6 years it has been shown that machine learning is a powerful tool to learn about the inside of faults from the acoustic emissions (AEs) that the fault broadcasts. For instance, the emissions can inform us of the instantaneous fault displacement and friction in addition to timing of an upcoming earthquake in laboratory experiments. Notably, it was further found that continuous seismic emissions contain a "fingerprint" for the phenomenon of slow slip in Earth. Here we show that by applying a deep learning approach that has shown striking results in natural language processing and computer vision applications, that the AEs also contain foreshadowing information about the immediate future of fault friction. Remarkably, the emissions can tell us if the laboratory fault is about to fail, and how it will begin to recover. This approach will help guide efforts in predicting earthquake timing in Earth. Key Points: The limits of deep learning models are explored for predicting the future frictional behavior of laboratory earthquake slipA deep learning transformer model is applied to acoustic emissions broadcast from the laboratory fault to predict future frictional stateAcoustic emissions are found to carry information for predicting future fault friction and imminence of failure [ABSTRACT FROM AUTHOR]

Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, ecological modelling, behavioural studies, DNA sequencing and population genetics and phylogenetics, among other applications. Deep learning relies on artificial neural networks for predictive modelling and excels at recognizing complex patterns.In this review we synthesize 818 studies using deep learning in the context of ecology and evolution to give a discipline‐wide perspective necessary to promote a rethinking of inference approaches in the field. We provide an introduction to machine learning and contrast it with mechanistic inference, followed by a gentle primer on deep learning. We review the applications of deep learning in ecology and evolution and discuss its limitations and efforts to overcome them. We also provide a practical primer for biologists interested in including deep learning in their toolkit and identify its possible future applications.We find that deep learning is being rapidly adopted in ecology and evolution, with 589 studies (64%) published since the beginning of 2019. Most use convolutional neural networks (496 studies) and supervised learning for image identification but also for tasks using molecular data, sounds, environmental data or video as input. More sophisticated uses of deep learning in biology are also beginning to appear.Operating within the machine learning paradigm, deep learning can be viewed as an alternative to mechanistic modelling. It has desirable properties of good performance and scaling with increasing complexity, while posing unique challenges such as sensitivity to bias in input data. We expect that rapid adoption of deep learning in ecology and evolution will continue, especially in automation of biodiversity monitoring and discovery and inference from genetic data. Increased use of unsupervised learning for discovery and visualization of clusters and gaps, simplification of multi‐step analysis pipelines, and integration of machine learning into graduate and postgraduate training are all likely in the near future. [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL) has been successfully applied in computer vision and natural language processing. The communication physical layer based on deep learning has received widespread attention. Introducing domain‐knowledge into neural networks (NNs), autoencoder based end‐to‐end communication system, incorporating radio transformer networks (RTNs) (RTNs‐AE) has achieved desirable performance with a channel model in the middle layer. The advent of RTNs underscores the power of expertise at DL. However, a tap‐length in the design of RTNs network must be assumed, which requires some channel information. To address this issue, a new deep learning wireless transceiver named pilot‐aided autoencoder (PA‐AE) is proposed. It can decode on a multipath fading channel without knowing the channel information and the equalization module design. The proposed scheme introduces a well‐designed auxiliary pilot, which carries the learned channel information into decoding with the transmitted signal. The decoding part recovers the sent information from the collected signal without specially designed modules for parameter estimation and equalization. [ABSTRACT FROM AUTHOR]

As resources for conservation are limited, gathering and analysing information from digital platforms can help investigate the global biodiversity crisis in a cost‐efficient manner. Development and application of methods for automated content analysis of digital data sources are especially important in the context of investigating human–nature interactions.In this study, we introduce novel application methods to automatically collect and analyse textual data on species of conservation concern from digital platforms. An end‐to‐end pipeline is constructed that begins from searching and downloading news articles about species listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) along with news articles from specific Twitter handles and proceeds with implementing natural language processing and machine learning methods to filter and retain only relevant articles. A crucial aspect here is the automatic annotation of training data, which can be challenging in many machine learning applications. A Named Entity Recognition model is then used to extract additional relevant information for each article.The data collected over a 1‐month period included 15,088 articles focusing on 585 species listed in Appendix I of CITES. The accuracy of the neural network to detect relevant articles was 95.91% while the Named Entity recognition model helped extract information on prices, location and quantities of traded animals and plants. A regularly updated database, which can be queried and analysed for various research purposes and to inform conservation decision making, is generated by the system.The results demonstrate that natural language processing can be used successfully to extract information from digital text content. The proposed methods can be applied to multiple digital data platforms at the same time and used to investigate human–nature interactions in conservation science and practice. [ABSTRACT FROM AUTHOR]

Objective: To develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing. Data Source: With respect to the linguistic content, we analyzed 283 216 secure messages sent by 6941 diabetes patients to physicians within an integrated system's electronic portal. Sociodemographic, clinical, and utilization data were obtained via questionnaire and electronic health records. Study Design: Retrospective study used natural language processing and machine learning to generate five unique "Literacy Profiles" by employing various sets of linguistic indices: Flesch‐Kincaid (LP_FK); basic indices of writing complexity, including lexical diversity (LP_LD) and writing quality (LP_WQ); and advanced indices related to syntactic complexity, lexical sophistication, and diversity, modeled from self‐reported (LP_SR), and expert‐rated (LP_Exp) health literacy. We first determined the performance of each literacy profile relative to self‐reported and expert‐rated health literacy to discriminate between high and low health literacy and then assessed Literacy Profiles' relationships with known correlates of health literacy, such as patient sociodemographics and a range of health‐related outcomes, including ratings of physician communication, medication adherence, diabetes control, comorbidities, and utilization. Principal Findings: LP_SR and LP_Exp performed best in discriminating between high and low self‐reported (C‐statistics: 0.86 and 0.58, respectively) and expert‐rated health literacy (C‐statistics: 0.71 and 0.87, respectively) and were significantly associated with educational attainment, race/ethnicity, Consumer Assessment of Provider and Systems (CAHPS) scores, adherence, glycemia, comorbidities, and emergency department visits. Conclusions: Since health literacy is a potentially remediable explanatory factor in health care disparities, the development of automated health literacy indicators represents a significant accomplishment with broad clinical and population health applications. Health systems could apply literacy profiles to efficiently determine whether quality of care and outcomes vary by patient health literacy; identify at‐risk populations for targeting tailored health communications and self‐management support interventions; and inform clinicians to promote improvements in individual‐level care. [ABSTRACT FROM AUTHOR]

Online discussion forums can be used for reflecting on the overall user experience of a system. If a user forum is well‐structured, it can be a valuable source of requirements‐related information, which can potentially be accommodated in the requirements engineering process to enhance the current and future software. However, presently, there are limited approaches for extracting such requirements‐related information from the relevant community forums. To fill this gap, this study proposes an automated approach, which automatically identifies requirements information using natural language processing and machine learning. For this purpose, the authors analysed 3319 user comments collected from the seven discussion topics in the Reddit forum. Then, using a content analysis approach, they studied how frequently end‐users submit such information across each discussion topic. Also, they developed an automated approach that identifies key stakeholders, who frequently contribute his rationales in the forum discussion. Further, they employed different machine learning algorithms to classify user comments into rationale elements of different types. The authors' results show that online forums, such as Reddit, can be a rich source of requirements elicitation. Also, machine learning is a promising tool to detect user's rationale and identify different kinds of requirements modelling elements. [ABSTRACT FROM AUTHOR]

Objective: Digital media conversations can provide important insight into the concerns and struggles of people with epilepsy (PWE) outside of formal clinical settings and help generate useful information for treatment planning. Our study aimed to explore the big data from open‐source digital conversations among PWE with regard to suicidality, specifically comparing teenagers and adults, using machine learning technology. Methods: Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying teenagers and adults who endorsed suffering from epilepsy and engaged in conversation about suicide. The search was limited to 12 months and included only conversations originating from US internet protocol (IP) addresses. Natural language processing and text analytics were employed to develop a thematic analysis. Results: A total of 222 000 unique conversations about epilepsy, including 9000 (4%) related to suicide, were posted during the study period. The suicide‐related conversations were posted by 7.8% of teenagers and 3.2% of adults in the study. Several critical differences were noted between teenagers and adults. A higher percentage of teenagers are: fearful of "the unknown" due to seizures (63% vs 12% adults), concerned about social consequences of seizures (30% vs 21%), and seek emotional support (29% vs 19%). In contrast, a significantly higher percentage of adults show a defeatist ("given up") attitude compared to teenagers (42% vs 4%). There were important differences in the author's determined sentiments behind the conversations among teenagers and adults. Significance: In this first of its kind big data analysis of nearly a quarter‐million digital conversations about epilepsy using machine learning, we found that teenagers engage in an online conversation about suicide more often than adults. There are some key differences in the attitudes and concerns, which may have implications for the treatment of younger patients with epilepsy. [ABSTRACT FROM AUTHOR]

Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin, and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high-dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading. Our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network. These results demonstrate that neuro-semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages. Hum Brain Mapp 38:6096-6106, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In the past few years, automatically generating descriptions for images has attracted a lot of attention in computer vision and natural language processing research. Among the existing approaches, data-driven methods have been proven to be highly effective. These methods compare the given image against a large set of training images to determine a set of relevant images, then generate a description using the associated captions. In this study, the authors propose to integrate an objectbased semantic image representation into a deep features-based retrieval framework to select the relevant images. Moreover, they present a novel phrase selection paradigm and a sentence generation model which depends on a joint analysis of salient regions in the input and retrieved images within a clustering framework. The authors demonstrate the effectiveness of their proposed approach on Flickr8K and Flickr30K benchmark datasets and show that their model gives highly competitive results compared with the state-of-the-art models. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine whether an expert system based on automated processing of electronic health records (EHRs) could provide a more accurate estimate of the annual rate of emergency department (ED) visits for suicide attempts in France, as compared to the current national surveillance system based on manual coding by emergency practitioners. A feasibility study was conducted at Lyon University Hospital, using data for all ED patient visits in 2012. After automatic data extraction and pre-processing, including automatic coding of medical free-text through use of the Unified Medical Language System, seven different machine-learning methods were used to classify the reasons for ED visits into 'suicide attempts' versus 'other reasons'. The performance of these different methods was compared by using the F-measure. In a test sample of 444 patients admitted to the ED in 2012 (98 suicide attempts, 48 cases of suicidal ideation, and 292 controls with no recorded non-fatal suicidal behaviour), the F-measure for automatic detection of suicide attempts ranged from 70.4% to 95.3%. The random forest and naïve Bayes methods performed best. This study demonstrates that machine-learning methods can improve the quality of epidemiological indicators as compared to current national surveillance of suicide attempts. [ABSTRACT FROM AUTHOR]

Distributional models of semantics learn word meanings from contextual co‐occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co‐occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co‐occurrences with vector accumulation. All of these models learned from positive information only: Words that occur together within a context become related to each other. A recent class of distributional models, referred to as neural embedding models, are based on a prediction process embedded in the functioning of a neural network: Such models predict words that should surround a target word in a given context (e.g., word2vec; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013). An error signal derived from the prediction is used to update each word's representation via backpropagation. However, another key difference in predictive models is their use of negative information in addition to positive information to develop a semantic representation. The models use negative examples to predict words that should not surround a word in a given context. As before, an error signal derived from the prediction prompts an update of the word's representation, a procedure referred to as negative sampling. Standard uses of word2vec recommend a greater or equal ratio of negative to positive sampling. The use of negative information in developing a representation of semantic information is often thought to be intimately associated with word2vec's prediction process. We assess the role of negative information in developing a semantic representation and show that its power does not reflect the use of a prediction mechanism. Finally, we show how negative information can be efficiently integrated into classic count‐based semantic models using parameter‐free analytical transformations. [ABSTRACT FROM AUTHOR]

The teaching and learning of algebraic word problems is a basic component of elementary education. Recently, to facilitate its learning, a few approaches for automatically solving algebraic and arithmetic word problems have been proposed. These systems generally use either natural language processing (NLP) or a combination of NLP and machine learning. However, they have low accuracy due to their large feature sets, extracted using limited preprocessing techniques. In this research work, we propose a template‐based approach that was developed by following a two‐step process. In the first step, we predict an equation template from a training dataset using NLP and a classification mechanism. The next step is to instantiate the predicted template with nouns and numbers through reasoning. To validate the proposed methodology, a prototype system was implemented. We then compared the proposed system with the existing systems using their respective datasets and the proposed dataset. The experimental results show improvement in accuracy, with an average precision of 80.6% and average recall of 83.5%. [ABSTRACT FROM AUTHOR]