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A hybrid deep learning approach for phenotype prediction from clinical notes

Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4503-4513页

作者： Khalafi, Sahar Ghadiri, Nasser Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Identifying patient cohorts from clinical notes in secondary use of electronic health records is a fundamental task in clinical information management. The patient cohort identification process requires identifying the patient phenotypes. However, the growing number of clinical notes makes it difficult to analyze the data manually. Therefore, automatically extracting clinical concepts is crucial to identify patient phenotypes correctly. This paper’s proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a Convolutional Neural Network (CNN) for identifying patients’ phenotypes in clinical notes. Furthermore, an extra CNN layer is run parallel to the hybrid proposed model to extract more features related to each phenotype. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding’s performance in identifying patient phenotypes. We also measured the effect of adding additional data cleaning steps on discharge reports to identify patient phenotypes using deep learning models. Results demonstrated the proposed hybrid model extracts more features than existing methods of patient phenotype extraction and provides a better F1-score. We show that complementing the proposed hybrid model with an extra CNN in identifying different phenotypes improves the F1 scores. In addition, eliminating punctuation, numbers, and stop words in discharge reports before training hybrid models increased model performance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Embeddings

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Research on Trash Detection Based on Instance Segmentation 3

Research on Trash Detection Based on Instance Segmentation

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Yu, Sheng Ye, Fei Informatics Department Beijing University of Technology Beijing China School of Transportation Science and Engineering Jilin Jianzhu University Jilin China

ISBN: (纸本)9781510667563

An improved hybrid task cascade (HTC) network for a multi-scale trash instance segmentation detection method is proposed to address the problems of ambiguous feature representation and low utilization of feature information in instance segmentation-based trash detection methods. First, multi-scale convolution is introduced in the backbone network based on the HTC network model to improve the backbone network's feature extraction capability for different sizes of trash. Second, improve the feature pyramid network structure by performing feature rescaling on feature channels based on the feature pyramid network, so that the network creates interactions across channels that do not change the spatial information, with the goal of reducing the influence of less important factors while using fewer parameters to achieve the best recognition degree. The experimental results show that the improved HTC network model can achieve robust trash feature extraction, improve detection performance, and significantly improve trash detection accuracy. © 2023 SPIE.

关键词： Feature extraction

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Many-Objective Optimization-Based Content Popularity Prediction for Cache-Assisted Cloud-Edge-End Collaborative IoT Networks

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IEEE Internet of Things Journal 2024年第1期11卷 1190-1200页

作者： Hu, Zhaoming Fang, Chao Wang, Zhuwei Tseng, Shu-Ming Dong, Mianxiong Faculty of Information Technology Beijing University of Technology Beijing100124 China Joint Innovation Laboratory of Digital GuangXi Smart Infrastructures Guangxi Information Center Nanning530000 China Department of Electronic Engineering National Taipei University of Technology Taipei106 Taiwan Department of Sciences and Informatics Muroran Institute of Technology Muroran050-0071 Japan

With the advancement of mobile communication technology, there has been a marked increase in the demand for personalized and ubiquitous Internet of Things (IoT) services, raising the expectations for network Quality of Service (QoS) and Quality of Experience (QoE). Existing popularity-prediction-based content caching policies improve QoS and QoE by precaching contents at the network edge, but jointly optimizing multiple network metrics remains a challenge. To address this challenge, we propose a many-objective optimization-based popularity prediction for cooperative caching (MaOPPC-Caching) framework for cloud-edge-end collaborative IoT networks. This framework simultaneously optimizes prediction accuracy, delay, offloaded traffic, and load balance. We integrate three prediction algorithms to forecast content popularity and present a horizontal and vertical collaborative caching decision strategy to generate caching forms based on the predicted results. Then, the many-objective evolutionary algorithm (MaOEA) is employed to optimize the combined proportions to take full advantage of hidden preferences and popularity characteristics of both users and items. To promote the convergence of the framework, we present a knowledge mining-based MaOEA (KMaOEA) to incorporate knowledge mining into the optimization process. Simulation results show that the proposed MaOPPC-Caching framework outperforms existing prediction algorithms in terms of four evaluation indicators. Furthermore, KMaOEA shows a significant advantage over NSGA-III in load balance, as indicated by a Mann-Whitney rank sum test with a p-value of 0.040. © 2023 IEEE.

关键词： Internet of things

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A Multi-Agent Deep-Reinforcement Learning Approach for Application-Agnostic Microservice Scaling

A Multi-Agent Deep-Reinforcement Learning Approach for Appli...

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2023 IEEE Virtual Conference on Communications, VCC 2023

作者： Fodor, Balazs Jakub, Akos Szucs, Gabor Sonkoly, Balazs Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics Hungary MTA-BME Network Softwarization Research Group Hungary HUN-REN-BME Cloud Applications Research Group Hungary

ISBN: (纸本)9798350318807

Efficient cloud-based microservice scaling needs to take into account the inter-dependencies between application components to avoid bottlenecks and to swiftly adapt to dynamically changing environments or user demands. Most of today's solutions are not adaptive enough especially to handle large-scale microservices. In this paper, we propose a novel solution leveraging Multi-Agent Deep Reinforcement Learning (MADRL). First, we define our model for horizontal scaling of microservices and formalize the problem. Second, we propose an algorithm based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve it. Third, a dedicated simulation environment is presented, where arbitrary microservices can be created for testing purposes, and we carry out a comprehensive evaluation. We analyze the performance of the model for microservices of different sizes, investigating its ability to optimize scaling while considering efficient resource utilization and application stability. Results show that our MADDPG-based RL algorithm outperforms the industry standard approach provided by Kubernetes' HPA by at least 14% in terms of resource usage cost. © 2023 IEEE.

关键词： Reinforcement learning

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Development and Characterization of Sn99Ag0.3Cu0.7-(ZrO2/CuO/TiO2) Nano-Composite Solder Alloys

Development and Characterization of Sn99Ag0.3Cu0.7-(ZrO2/CuO...

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2024 Pan Pacific Strategic Electronics Symposium, Pan Pacific 2024

作者： Illes, Balazs Choi, Halim Illes, Balazs Skwarek, Agata Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Electronics Technology Budapest Hungary Lukasiewicz Research Network Institute of Microelectronics and Photonics LTCC Technology Research Group Kraków Poland

ISBN: (纸本)9781944543204

The 'lead-free' change in the soldering technology caused the widespread application of the SnAgCu (SAC) solder alloys. The so-called low Ag content ones, like Sn99Ag0.3Cu0.7 (SAC0307) and Sn98.5Ag1Cu0.5 (SAC105), are more and more popular recently due to their good price and thermo-mechanical properties. The most novel solution to further improve the solder alloys' properties is adding nano-sized ceramic reinforcement particles into the solder alloys. This procedure results in, nano-composite'' solder joints. A wide range of ceramic nano-particles (NPs) was already tested, like Fe2O3, TiO2, Si3Ni4, ZrO2, Al2O3, SiC, La2O3, etc. The reinforcement particles improve the solder joints' mechanical properties (shear force, yield strength, microhardness, etc.), with some Kelvin of increase in the liquidus-Temperature of the alloy. It is generally accepted that mechanical improvement is caused by dispersion-strengthening mechanisms. The ceramic nano-particles are not soluble in the Sn, so they incorporate at the intermetallic (Cu6Sn5, CU3Sn, and Ag3Sn) grains and at the Sn grain boundaries, where they enhance heterogeneous nucleation. This effect suppresses the grain growth (all phases) and decreases dislocation motions. In the present study, the effect of three oxide ceramics (TiO2, ZrO2, and CuO) as reinforcements in 0.25wt% weight fraction was investigated on the quality and reliability of the composite SAC0307 solder alloy. The primary particle size of NPs was between 20-100nm. The NPs were mixed into the solder paste. Standard spread tests of the different alloys were done on FR4 laminates covered by Cu foil and imm-Ag surface finishing over on it. Solder joints were prepared from the alloys and 0603 chip resistors by convectional surface mounting technology on FR4-based printed circuit boards with imm-Ag surface finish. The shear strength of the chip resistors was measured. The solder joints were loaded with 4000 hours 85°C/85RH% climatic test to investigate their co

关键词： Titanium dioxide

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Guest Editorial: Introduction to Heterogeneous Robotic Systems in 5G and Beyond Networks

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IEEE Wireless Communications 2023年第2期30卷 64-65页

作者： Lu, Huimin Ho, Pin-Han Khalfi, Bassem Verikoukis, Christos Kyushu Institute of Technology Japan Department of Electrical and Computer Engineering The University of Waterloo Canada Oregon State University CorvallisOR United States University of Patras Department of Computer Engineering & Informatics Greece

Remote intelligence in the application of robotics and the autonomous system relies heavily on seamless wireless connections. The 5G mobile network technology meets traditional manufacturing enterprises' application requirements for wireless networks based on robot transformation and upgrading, robot interconnection, and remote interactive applications in production. However, there exists many challenging 5G communication issues, such as different communication protocols in the system varies with different robots and no dis-ruptive changes to the physical layer.

关键词： Special issues and sections Robots 5G mobile communication Physical layer Autonomous systems Heterogeneous networks

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Untangling the association between urban mobility and urban elements

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地球空间信息科学学报（英文版） 2024年第4期27卷 1071-1089页

作者： Jinzhou Cao Wei Tu Rui Cao Qili Gao Guanzhou Chen Qingquan Li College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina Key Laboratory of National Geographic Census and Monitoring Ministry of Natural ResourcesWuhan UniversityWuhanChina School of Architecture and Urban Planning & Guangdong Key Laboratory of Urban Informatics Shenzhen UniversityShenzhenChina Department of Land Surveying and Geo-Informatics & Smart Cities Research Institute The Hong Kong Polytechnic UniversityHong KongChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan UniversityWuhanChina

Understanding complex urban systems necessitates untangling the relationships between diverse urban elements such as population,infrastructure,and socioeconomic *** laws are basic but effective rules for evaluating a city's internal growth logic and assessing its efficiency by investigating whether urban indicators scale with *** date,only limited research has empirically explored the scaling relations between variables of urban mobility in mega-cities at an intra-urban scale of a few *** multiple urban-sensed and human-sensed data,this study proposes a thorough framework for quantifying the scaling laws in a *** begin,urban mobility networks are built by aggregating population flows using large-scale mobile phone tracking *** demonstrate the spatiotemporal variability of urban mobility,various network-based mobility measures are *** that,three different features of urban mobility laws are exposed,explaining spatial agglomeration,spatial hierarchical structures,and the temporal growth *** scaling correlations between urban indicators pertaining to socioeconomic features and infrastructure and a mobility-population measure are then quantified using multi-sourced urban-sensed *** this framework to the case study of Shenzhen,China revealed(a)spatial travel heterogeneity,hierarchical spatial structures,and mobility growth,and(b)not only a robust sub-linear relationship between infrastructure volume and population,but also a sub-linear relationship for socioeconomic *** identified scaling laws,both in terms of mobility measures and urban indicators,provide a multi-faceted portrait of the spatio-temporal varia-tions of urban settings,allowing us to better understand intra-urban developments and,consequently,provide critical policy evaluations and suggestions for improving intra-urban efficiency in the future.

关键词： Scaling law urban scaling urban mobility intra-urban system mobile phone data

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Development of IoT-Healthcare Model for Gastric Cancer from Pathological Images 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Belgaum, Mohammad Riyaz Momina, Shaik Maryam Farhath, L. Nousheen Nikhitha, K. Naga Jyothi, K. Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The diagnosis of stomach cancer automatically in digital pathology images is a difficult problem. Gastric cancer (GC) detection and pathological study can be greatly aided by precise region-by-region segmentation. On a technical level, this issue is complicated by the fact that malignant zones might be any size or shape and have fuzzy boundaries. The research employs a deep learning-based approach and integrates many bespoke modules to cope with these issues. The channel refinement model is the attentional actor on the chin channel. While implementing the feature channel, the learnt channel weight can be used to eliminate unnecessary features. Calibration is essential to improve classification precision. The results of channel recalibration can be improved with the help of a re-calibration (MSCR) model. The top pooling layer of the network is where the multiscale attributes are sent. The outcomes of channel recalibration may be enhanced by using the channel weights found at various scales as input to the next channel recalibration perfect. Our unique gastric cancer segmentation dataset, carefully glossed down to the pixel level by medical authorities, is used for extensive experimental comparisons. The numerical comparisons with other approaches show that our strategy is superior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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Depth and Pitch Control of Autonomous Underwater Glider Using Model Predictive Control with ARX Model

Depth and Pitch Control of Autonomous Underwater Glider Usin...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Jeremy, Jason Hidayat, Egi Muhammad Idris Siregar, Simon Trilaksono, Bambang Riyanto Habibullah, Natsir School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Telkom University Faculty of Applied Science Department Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350381290

Autonomous Underwater Gliders (AUGs) are extensively developed vehicles capable of prolonged exploration and observation in complex marine environments. Control of the AUG is challenging due to its slow response system and its constraints. In this research, a linear model representation of AutoRegressive eXogenous input (ARX) will be constructed using input and output data from the AUG system, built Model Predictive Control (MPC), analyzed the performance, comparing with traditional Proportional-Integral-Derivative (PID). The objective is to enhance setpoint tracking accuracy and minimize energy to extend exploration time while faced with constraints. Simulation results reveal MPC exhibits potential setpoint tracking, low overshoot as low as 0.6% up to 0.3m/s maximum depth rate, has relatively low input changes indicating good efficiency compared to PID. MPC approach effectively addresses slow response systems, managing momentum, and handling actuator constraints commonly encountered in AUG. © 2023 IEEE.

关键词： Model predictive control

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Online Transportation Order Matching Considering Passenger Priority Using Recency Frequency Monetary Method 6th

Online Transportation Order Matching Considering Passenger P...

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6th Asia Pacific Conference on Manufacturing System and the 5th International Manufacturing engineering Conference, APCOMS-IMEC 2022

作者： Handojo, Andreas Pujawan, Nyoman Santosa, Budi Singgih, Moses Laksono Industrial Engineering Department Sepuluh Nopember Institute of Technology Surabaya Indonesia Informatics Department Petra Christian University Surabaya Indonesia

ISBN: (纸本)9789819912445

With the development of transportation technology, online transportation is one of the choices of public transportation which is currently growing rapidly. Passengers can book transportation services through the app. The application will appoint a driver to fulfill the order. It takes a system that can prioritize service to loyal customers, to get priority to be served first. This is important in retaining loyal customers. In this research, the Recency Frequency Monetary (RFM) method was used to classify loyal customers. Based on this classification, drivers are appointed to fulfill orders from passengers (order matching). The method used to pair drivers and passengers is the Hungarian method. The simulation of this system will be tested using the Google Maps API. Based on the tests carried out, the results obtained that the system made was able to prioritize the fulfillment of orders from passengers based on its Recency Frequency Monetary value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Sales

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