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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yang Qiu Anwesh Mohanty Robert L. Owens Shamim Nemati Computer Science and Engineering University of California San Diego La Jolla USA Department of Biomedical Informatics University of California San Diego La Jolla USA Department of Medicine University of California San Diego La Jolla USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

To enhance sleep quality in hospitalized patients, we developed a conversational agent that streamlines the collection and analysis of sleep data. The system employs the Richards-Campbell Sleep Questionnaire, supplemented by additional questions about environmental factors such as room temperature and lighting, to comprehensively evaluate sleep disturbances experienced by patients. By processing patients’ spoken responses, the agent identifies environmental and care-related factors impacting sleep, which may allow for non-pharmacological interventions to enhance sleep quality. The integration of advanced conversational AI technologies, including GPT-4 and large language models, is a key feature of this system, enabling nuanced interpretation and structuring of patient feedback. This approach not only streamlines sleep assessment in hospital settings but also aligns with the shift towards patient-centric healthcare. By offering detailed insights into factors affecting sleep, the system showcases its potential with a high recognition accuracy, underscoring its potentially valuable role in advancing healthcare sleep quality management.

关键词： Surveys Accuracy Temperature Hospitals large language models Lighting Focusing Environmental factors Text to speech Quality assessment

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Deep Reinforcement Learning-Based Cloud-Edge Offloading for WBANs

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IEEE Transactions on Consumer Electronics 2024年

作者： Chen, Guihong Wang, Maojie Han, Shaodong Yin, Jiao Wang, Hua Cao, Jinli South China University of Technology School of Automation Science and Engineering Guangdong Guangzhou510641 China Guangdong Polytechnic Normal University School of Cyber Security Guangzhou510635 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3083 Australia Victoria University Institute for Sustainable Industries and Liveable Cities MelbourneVIC3083 Australia

Wireless body area networks (WBANs) are essential for real-time health monitoring using physiological sensors, but these sensors have limitations in computing power, storage, and battery life. To address these issues, we propose a cloud-edge computing architecture where data from sensors are initially processed by WBAN edge devices (smartphones, PDAs) before being offloaded to cloud servers for extensive analysis. We model the offloading decision as a Markov decision process (MDP) and design a system utility function that balances processing latency, power consumption, and the quality of communication. Traditional deep reinforcement learning algorithms like deep deterministic policy gradient (DDPG) optimize MDPs but face slow convergence and local optima issues. To overcome these, we introduce a multi-step deep deterministic policy gradient (MSDDPG) algorithm that uses a multi-step update mechanism, enhancing learning efficiency and solution accuracy. Experimental results show that MSDDPG significantly reduces processing delay and energy consumption while improving system utility. Additionally, our approach considers WBAN edge device constraints, ensuring practical applicability, extending device battery life, and enhancing performance in real-world scenarios. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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GA-Sense: Sensor placement strategy for detecting leaks in water distribution networks based on time series flow and genetic algorithm

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MethodsX 2024年 12卷 102612页

作者： Shiddiqi, Ary Mazharuddin lathifah, Artya Ahmad, Tohari Purwitasari, Diana Department of Informatics Institut Teknologi Sepuluh Nopember Indonesia Department of Computer Science and Information Technology La Trobe University Australia Department of Industrial and Information Management National Cheng Kung University Taiwan

The detection of leaks in time series flow systems is crucial for efficient and integrated industrial processes. This is especially true when daily demand patterns differ, as this results in fluctuations in the snapshots of water consumption that are commonly used as the basis for placing sensors to detect leaks. This paper introduces a novel method in which the genetic algorithm (GA) is applied to find optimal sensor locations and to enhance the accuracy of leak detection in time series flow data. The method consists of two steps. Firstly, the GA is used to identify the optimal sensor locations using a specific fitness function that accounts for flow patterns, system topology, and leak characteristics. The novelty of the proposed method lies in the weighting scheme of the fitness function, which takes into consideration the frequency of events and the magnitude of leaks at potential locations. Secondly, the selected sensor locations are integrated with an advanced time series data analysis to locate leaks. In this technique, the most consistently performing locations are dynamically selected over time, allowing the model to adapt to varying conditions to maintain optimal sensor placement. Experiments were conducted on a simulated time series flow system with known leak scenarios to evaluate the performance of the proposed method. The results demonstrated the superiority of our GA-based sensor placement strategy in terms of leak detection accuracy and efficiency compared to other methods. • We developed a model called GA-Sense for sensor placement strategy by considering flow patterns to maximize leak detection and localization capabilities. • GA-Sense uses time series data to find strategic sensor locations to identify abnormal flow patterns indicative of leaks. • This approach enhances the accuracy and efficiency of leak detection and localization compared to alternative methods. © 2024 The Author(s)

关键词： Genetic algorithm Leak detection and localization Sensor placement strategy Time series data Weighted fitness function

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Prediction of COVID-19 Transmission in the United States Using Google Search Trends

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computers, Materials & Continua 2022年第4期71卷 1751-1768页

作者： Meshrif Alruily Mohamed Ezz Ayman Mohamed Mostafa Nacim Yanes Mostafa Abbas Yasser El-Manzalawy College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Faculty of Engineering Al-Azhar UniversityCairo11651Egypt Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt RIADI Laboratory La Manouba UniversityManouba2010Tunisia Department of Translational Data Science and Informatics GeisingerDanvillePA17822USA

Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health *** to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the *** accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access *** objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related ***,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms *** the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,*** the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,*** 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,*** found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 *** also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.

关键词： Forecasting COVID-19 transmission and mortality in the US stacked LSTM SARS-COV-2 and google COVID-19 search trends

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Simulation and Experimental Research on Thermal-Mechanical Coupling Temperature Field in Micro-Milling Process of Micro-Channel

Simulation and Experimental Research on Thermal-Mechanical C...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： lanying Xu Sichen Feng Fangzheng Wu Qiang Wu Weichao Wang School of Mechatronic Engineering Guangdong Polytechnic Normal University Guangzhou China Department of computer science Tulane University LA USA

Micro milling is a new method for machining micro parts. In order to study the change rule of cutting temperature field in micro-milling process, the thermal-mechanical model of micro milling was established by using finite element analysis software. The influence of cutting parameters, milling mode and friction coefficient on the cutting temperature field is analyzed by simulating the micro-milling process and verified by orthogonal test. The results show that the simulation results are consistent with the experimental results. The spindle speed has the most significant effect on the cutting temperature, followed by the feed speed, and the amount of depth cutting is the least obvious; In the two different machining methods of forward milling and reverse milling, the reverse milling has a greater influence on the cutting temperature; when the friction coefficient is small, the range of cutting temperature rise is large, whereas the range of cutting temperature rise is small. The research results have certain guiding significance for the actual micro-milling.

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RoBERTa-BiLSTM: A Context-Aware Hybrid Model for Sentiment Analysis

arXiv

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arXiv 2024年

作者： Rahman, Md. Mostafizer Shiplu, Ariful Islam Watanobe, Yutaka Alam, Md. Ashad The Department of Computer and Information Systems University of Aizu Japan The Information and Communication Technology Cell Dhaka University of Engineering & Technology Gazipur Bangladesh The Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh The Ochsner Center for Outcomes Research New OrleansLA70121 United States

With the rapid advancement of technology and its easy accessibility, online activity has become an integral part of everyday human life. Expressing opinions, providing feedback, and sharing feelings by commenting on various platforms, including social media, education, business, entertainment, and sports, has become a common phenomenon. Effectively analyzing these comments to uncover latent intentions holds immense value in making strategic decisions across various domains. However, several challenges hinder the process of sentiment analysis including the lexical diversity exhibited in comments, the presence of long dependencies within the text, encountering unknown symbols and words, and dealing with imbalanced datasets. Moreover, existing sentiment analysis tasks mostly leveraged sequential models to encode the long dependent texts and it requires longer execution time as it processes the text sequentially. In contrast, the Transformer requires less execution time due to its parallel processing nature. In this work, we introduce a novel hybrid deep learning model, RoBERTa-BiLSTM, which combines the Robustly Optimized BERT Pretraining Approach (RoBERTa) with Bidirectional Long Short-Term Memory (BiLSTM) networks. RoBERTa is utilized to generate meaningful word embedding vectors, while BiLSTM effectively captures the contextual semantics of long-dependent texts. The RoBERTa-BiLSTM hybrid model leverages the strengths of both sequential and Transformer models to enhance performance in sentiment analysis. We conducted experiments using datasets from IMDb, Twitter US Airline, and Sentiment140 to evaluate the proposed model against existing state-of-the-art methods. Our experimental findings demonstrate that the RoBERTa-BiLSTM model surpasses baseline models (e.g., BERT, RoBERTa-base, RoBERTa-GRU, and RoBERTa-LSTM), achieving accuracies of 80.74%, 92.36%, and 82.25% on the Twitter US Airline, IMDb, and Sentiment140 datasets, respectively. Additionally, the model achieve

关键词： Sentiment analysis

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Forecasting the number of confirmed new cases of COVID-19 in Italy for the period from 19 May to 2 June 2020

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Infectious Disease Modelling 2021年第1期6卷 362-369页

作者： Marco Triacca Umberto Triacca Department of Mathematics Guido CastelnuovoUniversity of Rome“La Sapienza”Italy Department of Information Engineering Computer Science and MathematicsUniversity of L’AquilaItaly

In this paper we forecast the spread of the coronavirus disease 2019 outbreak in Italy in the time window from May 19 to June 2,*** particular,we consider the forecast of the number of new daily confirmed cases.A forecast procedure combining a log-polynomial model together with a first-order integer-valued autoregressive model is *** out-of-sample comparison with forecasts from an autoregressive integrated moving average(ARIMA)model is *** comparison indicates that our procedure outperforms the ARIMA *** Root Mean Square Error(RMSE)of the ARIMA is always greater than that of the our procedure and generally more than twice as high as the our procedure *** have also conducted Diebold and Mariano(1995)tests of equal mean square error(MSE).The tests results confirm that forecasts from our procedure are significantly more accurate at all *** think that the advantage of our approach comes from the fact that it explicitly takes into account the number of swabs.

关键词： COVID-19 Real-time forecasts Time series

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AN EFFICIENT ALGORITHM FOR THE RIEMANNIAN LOGARITHM ON THE STIEFEL MANIFOLD FOR A FAMILY OF RIEMANNIAN METRICS

arXiv

引用

arXiv 2024年

作者： Mataigne, Simon Zimmermann, Ralf Miolane, Nina UCLouvain ICTEAM Louvain-la-Neuve Belgium University of Southern Denmark Department of Mathematics and Computer Science Odense Denmark UC Santa Barbara Electrical and Computer Engineering Santa BarbaraCA United States

MSC Codes 15B10, 15B57, 53Z50, 65B99, 15A16Since the popularization of the Stiefel manifold for numerical applications in 1998 in a seminal paper from Edelman et al., it has been exhibited to be a key to solve many problems from optimization, statistics and machine learning. In 2021, Hüper et al. proposed a one-parameter family of Riemannian metrics on the Stiefel manifold, subsuming the well-known Euclidean and canonical metrics. Since then, several methods have been proposed to obtain a candidate for the Riemannian logarithm given any metric from the family. Most of these methods are based on the shooting method or rely on optimization approaches. For the canonical metric, Zimmermann proposed in 2017 a particularly efficient method based on a pure matrix-algebraic approach. In this paper, we derive a generalization of this algorithm that works for the one-parameter family of Riemannian metrics. The algorithm is proposed in two versions, termed backward and forward, for which we prove that it conserves the local linear convergence previously exhibited in Zimmermann’s algorithm for the canonical metric. © 2024, CC BY.

关键词： Geodesy

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Multilayer perceptron ensembles in a truly sparse training context

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Neural Computing and Applications 2025年

作者： van der Wal, Peter R. D. Strisciuglio, Nicola Azzopardi, George Mocanu, Decebal Constatin Information Systems Group Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh 9 Groningen9747 AG Netherlands Data Management and Biometrics Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Hallenweg 19 Enschede7522 NH Netherlands Department of Computer Science Faculty of Science Technology and Medicine University of Luxembourg Maison du Nombre 6 avenue de la Fonte Esch4364 Luxembourg

Ensemble learning for artificial neural networks (ANNs) is an effective method to enhance predictive performance. However, ANNs are computationally and memory intensive, and naively training multiple networks can lead to excessive training times and costs. An effective tool for improving ensemble efficiency is introducing topological sparsity. Even though several implementations of efficient ensembles have been proposed, none of them can provide actual benefits in terms of computational overhead as the sparsity is simulated using binary masks. In this paper, we address this issue by introducing a Truly Sparse Ensemble without binary masks and directly incorporate native sparsity. We also propose two algorithms for initializing new subnetworks within the ensemble, leveraging this native topological sparsity to enhance subnetwork diversity. We demonstrate the performance of the resulting models at high levels of sparsity on several datasets in terms of classification accuracy, floating point operations (FLOPs), and actual running time. The proposed methods outperform all baseline dense and truly sparse models on tabular data, successfully diversify the training trajectory of the subnetworks, and increase the topological distance between subnetworks after re-initialization. © The Author(s) 2025.

关键词： Digital arithmetic

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Prediction of Hourly Subway Ridership based on Artificial Intelligence Algorithms Using Weather Information

Prediction of Hourly Subway Ridership based on Artificial In...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： San Gwon Hyeon-Jeong Kim Ye-Jin Lee Jo-Eun Kim Seokheon Cho Department of Computer Science Andong National Univ Andong Gyeongsang South Korea Department of Computer Science and Engineering KonKuk Univ Seoul South Korea Department of Bio-medical Engineering Keimyung Univ Daegu South Korea Qualcomm Institute University of California San Diego La Jolla USA

This paper presents a model for predicting hourly subway ridership based on weather conditions. The goal is to provide efficient subway system service based on prediction model results. Three artificial intelligence algorithms, Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Multi-layer Perceptron (MLP), were used to compare and analyze the performance of our prediction model for hourly subway ridership. The performance of the prediction models was optimized across two subway stations with diverse and different regional characteristics rather than a single station. To compare the prediction results, 8 out of 15 initially considered independent variables were selected and used for performance enhancement according to the correlation and feature importance with the dependent variable. The outcomes indicated that the subway station heavily used by office workers is almost unaffected by weather variables, while the subway station used mostly by people enjoying leisure activities is more affected by weather variables. Across all datasets, the model using the MLP algorithm outperformed the alternative models in accurately predicting subway ridership.

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