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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Maruful Abedin Sumon Tor A. Kwemebe Venkata K. Melapu Md Mohiuddin Hasan Computational Data-Enabled Science and Engineering Jackson State University Jackson USA Department of Mathematics and Computer Science Jackson State University Jackson USA Computer Science and Engineering Jackson State University Jackson USA

Using electronic health records (EHR) data for predicting the condition of patients who are in need of emergency care is a promising application of machine learning. With the help of machine learning, complex problems of identification of patterns can be solved with ease and accuracy when proper machine learning model is implemented. This paper uses EHR data to build an end-to-end machine learning pipeline for predicting the outcome of emergency care patients. As a machine learning process, the pipeline includes data preprocessing, feature engineering, model selection, data set training and testing, evaluation, and deployment. By using EHR data in this pipeline, the model has been trained and tested on a large dataset of many patients information with different types of machine learning algorithms to improve and get the best prediction accuracy. This pipeline can aid in managing primary care patients efficiently and effectively with the implementation of best suitable machine learning algorithm at the time of urgency. By implementing the model on the EHR dataset, an accuracy of more than $75 \%$ is obtained which indicates the efficiency of the machine learning process in the process of identifying patients who need the emergency care more than the general patients when taken to the primary care or emergency.

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Maximum Effort Consensus Modeling Under Social Network Considering Individual Emotions 22nd

Maximum Effort Consensus Modeling Under Social Network Consi...

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22nd International Symposium on Knowledge and Systems sciences, KSS 2023

作者： Xu, Mengru Jing, Fuying Chao, Xiangrui Herrera-viedma, Enrique School of Management Science and Engineering Chongqing Technology and Business University Chongqing400067 China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing400067 China School of Business Sichuan University Chengdu610065 China Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain

ISBN: (纸本)9789819983179

It’s beyond disputed that everyone has their own unique understandings of words, which induces personalized individual semantics (PISs) attached to linguistic expressions. Social network illustrates trust relationships among group members. In linguistic social network decision making (SNGDM), social network analysis usually aids in determining the importance weights of decision makers (DMs). Actually, emotions may have an impact on trust propagation. For instance, positive emotions would strengthen trust whereas negative emotions work in a reverse way. Thus, a social network with individual emotions is firstly developed. Generally, DMs are frequently required to modify their opinions in consensus reaching process (CRP). To some extent, emotions also reflect their adjusted probabilities. Meanwhile, DM’s willingness and attitude to making necessary modifications for better agreement, that is measured by effort degree, will bring to different consensus results. Under a limited cost budget, a maximum effort consensus model driven by maximizing all DMs’ effort degree is proposed for instructing feedback regulation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Pre-trained language models in Spanish for health insurance coverage 5

Pre-trained language models in Spanish for health insurance ...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Aracena, Claudio Rodríguez, Nicolás Rocco, Victor Dunstan, Jocelyn Faculty of Physical and Mathematical Sciences University of Chile Chile Millennium Institute Foundational Research on Data Chile Chilean Safety Association Chile Department of Computer Science Catholic University of Chile Chile Institute for Mathematical and Computational Engineering Catholic University of Chile Chile Center for Mathematical Modeling University of Chile Chile

ISBN: (纸本)9781959429883

The field of clinical natural language processing (NLP) can extract useful information from clinical text. Since 2017, the NLP field has shifted towards using pre-trained language models (PLMs), improving performance in several tasks. Most of the research in this field has focused on English text, but there are some available PLMs in Spanish. In this work, we use clinical PLMs to analyze text from admission and medical reports in Spanish for an insurance and health provider to give a probability of no coverage in a labor insurance process. Our results show that fine-tuning a PLM pretrained with the provider's data leads to better results, but this process is time-consuming and computationally expensive. At least for this task, fine-tuning publicly available clinical PLM leads to comparable results to a custom PLM, but in less time and with fewer resources. Analyzing large volumes of insurance requests is burdensome for employers, and models can ease this task by pre-classifying reports that are likely not to have coverage. Our approach of entirely using clinical-related text improves the current models while reinforcing the idea of clinical support systems that simplify human labor but do not replace it. To our knowledge, the clinical corpus collected for this study is the largest one reported for the Spanish language. © 2023 Association for computational Linguistics.

关键词： Health insurance

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Enhanced Bitcoin Price Prediction Using RNN-GRU Algorithm with Optimized Parameters: Overcoming LSTM Ambiguities for Improved Accuracy and Efficiency

Enhanced Bitcoin Price Prediction Using RNN-GRU Algorithm wi...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： M. Kathiravan Meenakshi M Kaviya M S. Sreesubha Sathyadurga. V V. Neela Gandhi Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of CSE Department of Computational Intelligence SRM Institute of Science and Technology Chennai India Department of Artificial Intelligence And Data Science Panimalar Engineering College Chennai Department of AIML Rajalakshmi Engineering College Thandalam Chennai Department of CSE Hindustan Institute of Technology and Science padur Chennai Department of CSE Panimalar Engineering college Chennai

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Bitcoin's decentralised character and introduction in January 2009 can be mostly attributed to Satoshi Nakamoto, the unidentified designer of the digital money. Unlike regular money, Bitcoin has no value in and of itself and is not supported by any government or financial institution. The “mining” process consumes a lot of resources thus it cannot manage trades without a network of computers, often known as “nodes” or “miners.” The Bitcoin blockchain, a catalogue of all the past transactions, is maintained by these nodes. Like equities, bitcoin is getting more and more well-known even if it is somewhat erratic as an investment. Its value varies greatly; hence it is difficult to predict when prices will adjust. Since Bitcoin is so erratic, automated approaches are growing more crucial for future projections regarding it. Often utilised for prediction, long short-term memory (LSTM) networks have constraints. LSTM focusses on short-term input and has a complex and occasionally ambiguous design, thus some claim it does not operate well when prior data is not significant to current forecasts. This has caused some to wonder if one can forecast Bitcoin's price using this approach. Made as a substitute for standard RNNs, the Gated Recurrent Unit (GRU) helps to prevent the fading gradient issue. Bitcoin price projections made by the GRU design are far more accurate than those derived from more antiquated methods.

关键词： Accuracy Time series analysis Bitcoin Blockchains Convolutional neural networks Probes Long short term memory Standards Random forests Investment

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Exploring Television White Space as an Alternative for Wireless Broadband Connectivity 2

Exploring Television White Space as an Alternative for Wirel...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Ifijeh, Ayodele H. Adetiba, Emmanuel Adewale, Adeyinka Thakur, Surrendra Moyo, Sibusiso Emmanuel, Dare O. Ukpong, Udeme C. Dept. of Electrical and Information Engineering Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Covenant University Dept. of Electrical and Information Engineering Ota Canaanland Nigeria Durban University of Technology KZN E-skills CoLab Durban4001 South Africa School for Data Science and Computational Thinking Stellenbosch University Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358834

Advancement in wireless transmission has enabled the communication among multiple devices all over the world, as well as expanding the frontiers of wireless Internet access. As a result of these advancement, numerous devices are questing for wireless connectivity thereby congesting the existing spectrum. If new measures are not taken to reduce depletion of the reserved radio resource, new allocations of wireless Internet access will be impossible, thereby risking the development of the entire wireless ecosystem. The demands for resourceful spectrum consumption have continually driven researchers and stakeholders in the wireless industry to search for spare radio spectrum to offer affordable and reliable wireless connectivity. The TV broadcasting is not fully utilizing the spectrum in some geographical zone. These TV unused spaces called TV White Spaces (TVWS), are well-thought-out to be the probable future resolutions for unavailability of spectrum. The low frequency band (470 to 694 MHz) of the unutilized TV channel has exceptional broadcast features over extensive distances and enables transmissions through high-rise buildings, trees, and mountains. These features allow the applications of TVWS in Internet of Things (IoT), wireless sensor network, wireless connectivity, wireless switching, smart grid and cities, automated transportation, and logistics as well as connectivity in cloud infrastructure. This paper presents a study on the advantages and benefits of television white space as an alternative to wireless broadband connectivity. © 2023 IEEE.

关键词： Tall buildings

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Analyzing α-divergence in Gaussian Rate-Distortion-Perception Theory

Analyzing α-divergence in Gaussian Rate-Distortion-Percepti...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Martha V. Sourla Giuseppe Serra Photios A. Stavrou Marios Kountouris School of Electrical & Computer Engineering Technical University of Crete Chania Greece Communication Systems Department EURECOM France Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

The problem of the estimation of the information rate-distortion-perception function (RDPF), which is a relevant information-theoretic quantity in goal-oriented lossy compression and semantic information reconstruction, is investigated here. Specifically, we study the RDPF tradeoff for Gaussian sources subject to a mean-squared error (MSE) distortion and a perception measure that belongs to the family of α-divergences. Assuming a jointly Gaussian RDPF, which forms a convex optimization problem, we characterize an upper bound for which we find a parametric solution. We show that evaluating the optimal parameters of this parametric solution is equivalent to finding the roots of a reduced exponential polynomial of degree α. Additionally, we determine which disjoint sets contain each root, which enables us to evaluate them numerically using the well-known bisection method. Finally, we validate our analytical findings with numerical results and establish connections with existing results.

关键词： Wireless communication Upper bound Semantics Measurement uncertainty Estimation Distortion Polynomials Convex functions Distortion measurement Information theory

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Asymptotic properties of Vecchia approximation for Gaussian processes

arXiv

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arXiv 2024年

作者： Kang, Myeongjong Schäfer, Florian Guinness, Joseph Katzfuss, Matthias Department of Statistics Texas A&M University United States School of Computational Science and Engineering Georgia Institute of Technology United States Department of Statistics and Data Science Cornell University United States Department of Statistics University of Wisconsin–Madison United States

Vecchia approximation has been widely used to accurately scale Gaussian-process (GP) inference to large datasets, by expressing the joint density as a product of conditional densities with small conditioning sets. We study fixed-domain asymptotic properties of Vecchia-based GP inference for a large class of covariance functions (including Matérn covariances) with boundary conditioning. In this setting, we establish that consistency and asymptotic normality of maximum exact-likelihood estimators imply those of maximum Vecchia-likelihood estimators, and that exact GP prediction can be approximated accurately by Vecchia GP prediction, given that the size of conditioning sets grows polylogarithmically with the data size. Hence, Vecchia-based inference with quasilinear complexity is asymptotically equivalent to exact GP inference with cubic complexity. This also provides a general new result on the screening effect. Our findings are illustrated by numerical experiments, which also show that Vecchia approximation can be more accurate than alternative approaches such as covariance tapering and reduced-rank approximations. Copyright © 2024, The Authors. All rights reserved.

关键词： Kriging

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Performance Disparities between Accents in Automatic Speech Recognition 37

Performance Disparities between Accents in Automatic Speech ...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： DiChristofano, Alex Shuster, Henry Chandra, Shefali Patwari, Neal Division of Computational & Data Sciences Washington University St. Louis United States Department of Computer Science & Engineering Washington University St. Louis United States Department of Women Gender and Sexuality Studies Washington University St. Louis United States Department of History Washington University St. Louis United States Department of Electrical & Systems Engineering Washington University St. Louis United States

ISBN: (纸本)9781577358800

In this work, we expand the discussion of bias in Automatic Speech Recognition (ASR) through a large-scale audit. Using a large and global data set of speech, we perform an audit of some of the most popular English ASR services. We show that, even when controlling for multiple linguistic covariates, ASR service performance has a statistically significant relationship to the political alignment of the speaker's birth country with respect to the United States' geopolitical power. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Speech recognition

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Incorporating 5G NR Sidelink with MQTT to Maximize Energy Efficiency in IoT-Enabled Microgrids

Incorporating 5G NR Sidelink with MQTT to Maximize Energy Ef...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： G Neelamegam Hermina JM Ashforn Mohamed S Raja J. Priyadharshini J Akshya Mani Deepak Choudhry M. Sundarrajan Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Department of Computer Science and Engineering Kalaignarkarunanidhi Institute of Technology Coimbatore Department of Information Technology K. Ramakrishnan College of Engineering Trichy Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

IoT for smart microgrids is an innovative disruption at the cutting edge of energy management, relying on interconnectedness provided by smart sensors and devices to enhance power generation and distribution precision in metropolitan areas. The problems of the real-time operational capabilities required for efficient energy resource management thus progress, inhibit rapid control and management. The main goal of this research proposal consists of to use of the advanced technology of 5G NR Sidelink combined with MQTT to conduct a revolutionary communication system of Microgrid running through the IoT devices. The 5G NR (New Radio) Sidelink enables direct D2D communication, which leads to a dramatic lowering of the latency in the absence of traditional network infrastructures for communication, while the MQTT protocol improves the communication efficiency among networks of numerous IoT devices. The introduction of this system was followed by a 15% decrease in energy transmission losses and by 20% improvement in response times in comparison to conventional models specified in the quantitative evaluation thus increasing operational efficiency by far. These efficiencies not only reduce operating expenses and power consumption but also increase the solution's scalability, pointing out the possibility of building the solution for bigger urban microgrid implementations. In consideration of that, the suggested mechanism with 5G NR Sidelink along with MQTT overcomes elaborate barriers of IoT-based microgrids and establishes a new potency in advanced energy management in smart cities which could make the distribution network in the urban areas on the edge of the verge of a change.

关键词： Renewable energy sources 5G mobile communication Scalability Microgrids Sidelink Propagation losses Smart grids

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A Residual Multi-Scale Convolutional Neural Network with Transformers for Speech Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 915-932页

作者： Yan, Tianhao Meng, Hao Parada-Cabaleiro, Emilia Tao, Jianhua Li, Taihao Schuller, Bjorn W. Zhejiang Lab Research Center for Data Hub and Security Hangzhou311121 China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin150001 China Johannes Kepler University Linz Institute of Computational Perception Linz4040 Austria Tsinghua University Department of Automation Beijing100084 China UCAS Hangzhou Institute for Advanced Study Hangzhou310024 China University of Augsburg Chair of Embedded Intelligence for Health Care & Wellbeing Augsburg86159 Germany Imperial College London Group on Language Audio & Music LondonSW7 2AZ United Kingdom

The great variety of human emotional expression as well as the differences in the ways they perceive and annotate them make Speech Emotion Recognition (SER) an ambiguous and challenging task. With the development of deep learning, long-term progress has been made in SER systems. However, the existing convolutional neural networks present certain limitations, such as their inability to well capture features, which contain important emotional information. Moreover, the position encoding in the Transformer structure is relatively fixed and only encodes the time domain dimension, which cannot effectively obtain the position information of discriminative features in the frequency domain dimension. In order to overtake these limitations, we propose an end-to-end Residual Multi-Scale Convolutional Neural Networks (RMSCNN) with Transformer model network. Simultaneously, to further validate the effectiveness of RMSCNN in extracting multi-scale features and delivering pertinent emotion localization data, we developed the RMSC_down network in conjunction with the Wav2Vec 2.0 model. The results of the prediction of Arousal, Valence and Dominance on the popular corpora demonstrate the superiority and robustness of our approach for SER , showing an improvement of the recognition accuracy in the public dataset MSP-Podcast 1.9 version. The code is available at this GitHub. © 2010-2012 IEEE.

关键词： Convolutional neural networks

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