In the world, the technological and industrial revolution is accelerating by the widespread application of new generation information and communication technologies, such as AI, IoT (the Internet of ...Things), and blockchain technology. Artificial intelligence has attracted much attention from government, industry, and academia. In this study, popular articles published in recent years that relate to artificial intelligence are selected and explored. This study aims to provide a review of artificial intelligence based on industry information integration. It presents an overview of the scope of artificial intelligence using background, drivers, technologies, and applications, as well as logical opinions regarding the development of artificial intelligence. This paper may play a role in AI-related research and should provide important insights for practitioners in the real world.The main contribution of this study is that it clarifies the state of the art of AI for future study.
Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of machine learning ...models. However, BO is practically limited to optimizing 10–20 parameters. To scale BO to high dimensions, we usually make structural assumptions on the decomposition of the objective and/or exploit the intrinsic lower dimensionality of the problem, e.g. by using linear projections. We could achieve a higher compression rate with nonlinear projections, but learning these nonlinear embeddings typically requires much data. This contradicts the BO objective of a relatively small evaluation budget. To address this challenge, we propose to learn a low-dimensional feature space jointly with (a) the response surface and (b) a reconstruction mapping. Our approach allows for optimization of BO’s acquisition function in the lower-dimensional subspace, which significantly simplifies the optimization problem. We reconstruct the original parameter space from the lower-dimensional subspace for evaluating the black-box function. For meaningful exploration, we solve a constrained optimization problem.
With the multiplication of social media platforms, which offer anonymity, easy access and online community formation and online debate, the issue of hate speech detection and tracking becomes a ...growing challenge to society, individual, policy-makers and researchers. Despite efforts for leveraging automatic techniques for automatic detection and monitoring, their performances are still far from satisfactory, which constantly calls for future research on the issue. This paper provides a systematic review of literature in this field, with a focus on natural language processing and deep learning technologies, highlighting the terminology, processing pipeline, core methods employed, with a focal point on deep learning architecture. From a methodological perspective, we adopt PRISMA guideline of systematic review of the last 10 years literature from ACM Digital Library and Google Scholar. In the sequel, existing surveys, limitations, and future research directions are extensively discussed.
Attention in Natural Language Processing Galassi, Andrea; Lippi, Marco; Torroni, Paolo
IEEE transaction on neural networks and learning systems,
10/2021, Volume:
32, Issue:
10
Journal Article
Open access
Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced ...advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures in natural language processing, with a focus on those designed to work with vector representations of the textual data. We propose a taxonomy of attention models according to four dimensions: the representation of the input, the compatibility function, the distribution function, and the multiplicity of the input and/or output. We present the examples of how prior information can be exploited in attention models and discuss ongoing research efforts and open challenges in the area, providing the first extensive categorization of the vast body of literature in this exciting domain.
Natural Language Processing utilization in Healthcare Hudaa, Syihaabul; Setiyadi, Dwi Bambang Putut; Lydia, E. Laxmi ...
International journal of engineering and advanced technology,
10/2019, Volume:
8, Issue:
6s2
Journal Article
Open access
The significance of consolidating Natural Language Processing (NLP) techniques in clinical informatics research has been progressively perceived over the previous years, and has prompted ...transformative advances. Ordinarily, clinical NLP frameworks are created and assessed on word, sentence, or record level explanations that model explicit traits and highlights, for example, archive content (e.g., persistent status, or report type), record segment types (e.g., current meds, past restorative history, or release synopsis), named substances and ideas (e.g., analyses, side effects, or medicines) or semantic qualities (e.g., nullification, seriousness, or fleetingness). While some NLP undertakings consider expectations at the individual or gathering client level, these assignments still establish a minority. Here we give an expansive synopsis and layout of the difficult issues engaged with characterizing suitable natural and outward assessment strategies for NLP look into that will be utilized for clinical results research, and the other way around. A specific spotlight is set on psychological wellness investigate, a zone still generally understudied by the clinical NLP look into network, however where NLP techniques are of prominent importance. Ongoing advances in clinical NLP strategy improvement have been huge, yet we propose more accentuation should be put on thorough assessment for the field to progress further. To empower this, we give noteworthy recommendations, including an insignificant convention that could be utilized when announcing clinical NLP strategy improvement and its assessment.
The difficulty of accurately summarising Assamese text content is a significant barrier in natural language processing (NLP). Manually summarising lengthy Assamese texts is time-consuming and ...labor-intensive. As a result, automatic text summarization has developed as a critical NLP study topic. In this study, we integrate the Transformer and Self-Attention approaches to develop an abstract text summarization model. This Transformer-based technique uses self-attention approaches to successfully manage co-reference concerns in Assamese text, enhancing overall system understanding. This proposed approach improves the efficiency of text summarization greatly. We exhaustively evaluated the model using the Assamese dataset (AD-50), which contains human-produced summaries, to assess its performance. When compared to current state-of-the-art baseline models, our model outperformed them. On the AD-50 dataset, for example, our suggested model obtained a low training loss of 0.0022 during 20 training epochs, as well as an amazing model accuracy of 47.15 percentage. This research marks a substantial advancement in the field of Assamese abstractive text summarization, with intriguing implications for practical applications in NLP.
The spread of false information in the digital age has led to serious threats about the veracity and dependability of information traded online. Fake news can exacerbate social and political ...divisions, as it often targets specific groups or promotes divisive narratives. This paper sheds light on the pioneering venture into the field of fake news detection in Assamese, filling a significant research gap that had previously remained unaddressed. A comprehensive dataset of 6,000 news articles was meticulously collected to aid with the proposed research. The dataset contains 3,000 true news items and an equal number of fraudulent news stories. A fake news detection system for the Assamese language was created in this work using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) models. The accuracy of the Bi-LSTM model was 94.17%, compared to the LSTM model’s 93.50%. This research establishes a foundational framework for fake news detection in Assamese. The findings demonstrate the potential of LSTM and Bi-LSTM models for effectively preventing the propagation of fake news in the Assamese language and improving the accuracy and reliability of information in the digital sphere.
Psoriasis is an immune-mediated skin disease affecting approximately 3% of the global population. Proper management of this condition necessitates the assessment of the Body Surface Area (BSA) and ...the involvement of nails and joints. Recently, the integration of Natural Language Processing (NLP) with Electronic Medical Records (EMRs) has shown promise in advancing disease classification and research. This study evaluates the performance of ChatGPT-4, a commercial AI platform, in analyzing unstructured EMR data of psoriasis patients, particularly in identifying affected body areas.
The study analyzed EMR data from 94 patients treated at the Dermatology Department and Psoriasis Outpatient Clinic of Sheba Medical Center between 2008 and 2022. The data were processed using the ChatGPT-4 interface to identify and report the body areas affected by psoriasis. These identified areas were then categorized, and the accuracy of ChatGPT-4′s analysis was compared with that of a senior dermatologist.
The results revealed that the dermatologist identified 477 psoriasis-affected body areas. ChatGPT-4 accurately recognized 443 (92.8%) of these areas, missed 34, and incorrectly identified 30 areas as affected. From 94 cases, nail involvement was detected in 32 cases (34.0%), with ChatGPT-4 correctly identifying 29 cases. Joint involvement was noted in 25 cases (26.6%), with 24 correctly identified by ChatGPT-4. Complete accuracy was achieved in 54 cases (57.4%), while inaccuracies were observed in 40 cases (42.6%). We found that cases with more characters, words, or identified body areas were more prone to errors, suggesting that increased data complexity heightens the likelihood of inaccuracies in AI analysis.
ChatGPT-4 demonstrated a high performance in analyzing detailed and complex unstructured EMR data from psoriasis patients, effectively identifying involved body areas, including nails and joints. This highlights the potential of NLP algorithms in enhancing the analysis of unstructured EMR data for both clinical follow-up and research purposes.
Despite the increasing use of natural language processing (NLP) in the construction domain, no systematic comparison has been conducted between NLP applications in construction and the latest ...advancements in NLP within the computer science domain. Therefore, this study compares NLP studies in these two domains. Firstly, a bibliometric analysis was performed on 55 publications in state-of-the-art NLP studies, which identified four main research areas in NLP. Secondly, a systematic review of 202 NLP studies in construction was conducted, presenting representative application areas of NLP and their current technical status. The results reveal a decreasing technology gap between NLP in construction and the state-of-the-art. However, the comparison also highlighted gaps in application areas and methodologies, and eight future research opportunities were proposed. This review serves as a foundation for future studies that aim to apply state-of-the-art NLP technologies in the construction domain.
•Systematic comparison was conducted between NLP studies in construction and state-of-the-art.•State-of-the-art NLP methods and the major field of applications (i.e., NLP tasks) were reviewed.•PRISMA and bibliometic analysis were used for the review study.•Technology gaps between NLP in construction and state-of-the-art were presented.•Future research opportunities were suggested to fill the identified technology gaps.
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