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21st international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Li, Yang Li, Qianmu Meng, Shunmei Hou, Jun Nanjing Univ Sci & Technol Nanjing Peoples R China Nanjing Vocat Univ Ind Technol Sch Social Sci Nanjing Peoples R China Wuyi Univ Intelligent Mfg Dept Jiangmen Peoples R China

ISBN: (纸本)9783030953843;9783030953836

As a significant application of Big data, recommender system can effectively solve information overload. The user's behavior sequence forms massive data and has excellent mining value. Sequential recommendation is to extract user's features in massive sequential data and predict the next interaction based on the user's recent temporal behavior. Currently, recurrent neural networks (RNN) and Graph Neural Networks (GNN) take on the role of item embedding in sequential recommendation and have shown adequate performance. However, such RNN based model and GNN based model cannot deeply mine the complex behavior sequence and neglect user preference like rating information. Inspired by the popular Transformer, we adopt the Transformer encoder layer to process sequence and represent item embedding by multi-head attention. Meanwhile, rating information is integrated into weight calculation when we represent the user preference with self-attention. Weight with rating not only retains the structural information of sequence but also combines the user's preferences. What's more, we consider global and local preferences to formulate hybrid performance and make recommendations in Top-N. For persuasiveness, we conduct experiments on large real-world datasets, and our model performs better in most cases on two datasets compared to state-of-the-art methods.

关键词： Recommender system Sequential recommendation Transformer Attention mechanism Big data application data analytics

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Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data

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international JOURNAL OF MEDICAL INFORMATICS 2024年 189卷 105506-105506页

作者： Seinen, Tom M. Kors, Jan A. van Mulligen, Erik M. Fridgeirsson, Egill A. Verhamme, Katia M. C. Rijnbeek, Peter R. Erasmus MC Dept Med Informat Dr Molewater Pl 40 NL-3015 GD Rotterdam Netherlands

Objective: Observational studies using electronic health record (EHR) databases often face challenges due to unspecific clinical codes that can obscure detailed medical information, hindering precise data analysis. In this study, we aimed to assess the feasibility of refining these unspecific condition codes into more specific codes in a Dutch general practitioner (GP) EHR database by leveraging the available clinical free text. Methods: We utilized three approaches for text classification-search queries, semi-supervised learning, and supervised learning-to improve the specificity of ten unspecific international Classification of Primary Care (ICPC-1) codes. Two text representations and three machine learning algorithms were evaluated for the (semi-) supervised models. Additionally, we measured the improvement achieved by the refinement process on all code occurrences in the database. Results: The classification models performed well for most codes. In general, no single classification approach consistently outperformed the others. However, there were variations in the relative performance of the classification approaches within each code and in the use of different text representations and machine learning algorithms. Class imbalance and limited training data affected the performance of the (semi-)supervised models, yet the simple search queries remained particularly effective. Ultimately, the developed models improved the specificity of over half of all the unspecific code occurrences in the database. Conclusions: Our findings show the feasibility of using information from clinical text to improve the specificity of unspecific condition codes in observational healthcare databases, even with a limited range of machine-learning techniques and modest annotated training sets. Future work could investigate transfer learning, integration of structured data, alternative semi-supervised methods, and validation of models across healthcare settings. The improved level of de

关键词： Electronic Health Record Machine Learning Natural Language processing Primary Health Care Text Mining

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Reproducibility of predictive networks for mouse visual cortex 38

Reproducibility of predictive networks for mouse visual cort...

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38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Turishcheva, Polina Burg, Max F. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science University Göttingen Campus Institute Data Science Germany International Max Planck Research School for Intelligent Systems Tübingen Germany Tübingen AI Center University of Tübingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Deep predictive models of neuronal activity have recently enabled several new discoveries about the selectivity and invariance of neurons in the visual cortex. These models learn a shared set of nonlinear basis functions, which are linearly combined via a learned weight vector to represent a neuron's function. Such weight vectors, which can be thought as embeddings of neuronal function, have been proposed to define functional cell types via unsupervised clustering. However, as deep models are usually highly overparameterized, the learning problem is unlikely to have a unique solution, which raises the question if such embeddings can be used in a meaningful way for downstream analysis. In this paper, we investigate how stable neuronal embeddings are with respect to changes in model architecture and initialization. We find that L1 regularization to be an important ingredient for structured embeddings and develop an adaptive regularization that adjusts the strength of regularization per neuron. This regularization improves both predictive performance and how consistently neuronal embeddings cluster across model fits compared to uniform regularization. To overcome overparametrization, we propose an iterative feature pruning strategy which reduces the dimensionality of performance-optimized models by half without loss of performance and improves the consistency of neuronal embeddings with respect to clustering neurons. Our results suggest that to achieve an objective taxonomy of cell types or a compact representation of the functional landscape, we need novel architectures or learning techniques that improve identifiability. The code is available https://***/pollytur/readout_reproducibility. © 2024 Neural information processing systems foundation. All rights reserved.

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Naturalistic Driving Scenario Recognition with Multimodal data 23

Naturalistic Driving Scenario Recognition with Multimodal Da...

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23rd IEEE international conference on Mobile data Management (IEEE MDM)

作者： Wang, Ke Yang, Jie Li, Zhe Liu, Yiyang Xue, Junxiao Liu, Hao Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou 450001 Peoples R China

ISBN: (数字)9781665451765

ISBN: (纸本)9781665451765

Driving Scenario recognition is one fundamental technology of automated driving systems or advanced driver assistance systems. A common practice of driving scenario recognition is to conduct classification tasks with the data collected by in-vehicle data acquisition system or driving simulator. In most existing works, visual data were used since the relevant information of driving scenarios is usually inferable from their visual appearance. However, the non-visual information, e.g. physiological state of the driver, also provide complementary information for the scenario recognition task especially when the visual appearance is insufficient to differentiate similar naturalistic scenarios in some cases. In this paper, we propose a hybrid driving scenario recognition model with multimodal input. The model consists of a convolutional neural network based visual data sub-model, a stacked autoencoder based physiological data sub-model, and a fusion sub-model that combines the extracted features from both visual and physiological sub-models. Besides, a post-processing is adopted to correct the recognition results of some ambiguous scenarios. Experimental results on the trip data collected in the naturalistic driving context demonstrated the effectiveness of the proposed method.

关键词： Driving scenarios recognition multimodal data data fusion pseudoinverse learning deep learning

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Citizen Science for the Sea with Information Technologies: An Open Platform for Gathering Marine data and Marine Litter Detection from Leisure Boat Instruments 19

Citizen Science for the Sea with Information Technologies: A...

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19th IEEE international conference on e-Science, e-Science 2023

作者： De Vita, Ciro Giuseppe Mellone, Gennaro Sanchez-Gallegos, Dante D. Coviello, Giuseppe Romano, Diego Lapegna, Marco Ciaramella, Angelo University of Naples 'Parthenope' Dept. of Science and Technology Naples Italy University of Madrid Carlos Iii Dept. of Computer Science Madrid Spain Nec Laboratories of America Inc. PrincetonNJ United States Naples Italy University of Naples 'Federico II' Dept. of Mathematics and Applications Naples Italy

ISBN: (纸本)9798350322231

data crowdsourcing is an increasingly pervasive and lifestyle-changing technology due to the flywheel effect that results from the interaction between the Internet of Things and Cloud Computing. This paper presents the Citizen Science for the Sea with Information Technologies (C4Sea-IT) framework. It is an open platform for gathering marine data from leisure boat instruments. C4Sea-IT aims to provide a coastal marine data gathering, moving, processing, exchange, and sharing platform using the existing navigation instruments and sensors for today's leisure and professional vessels. In this work, a use case for the detection and tracking of marine litter is shown. The final goal is weather/ocean forecasts argumentation with Artificial Intelligence prediction models trained with crowdsourced data. © 2023 IEEE.

关键词： Boat instruments

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data models for an Imaging Bio-bank for Colorectal, Prostate and Gastric Cancer: the NAVIGATOR Project

Data Models for an Imaging Bio-bank for Colorectal, Prostate...

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4th IEEE-EMBS international conference on Wearable and Implantable Body Sensor Networks (BSN) / 18th IEEE-EMBS international conference on Biomedical and Health Informatics (BHI)

作者： Berti, Andrea Carloni, Gianluca Colantonio, Sara Pascali, M. Antonietta Manghi, Paolo Pagano, Pasquale Buongiorno, Rossana Pachetti, Eva Caudai, Claudia di Gangi, Domenico Carlini, Emanuele Falaschi, Zeno Ciarrocchi, Esther Neri, Emanuele Bertelli, Elena Miele, Vittorio Carpi, Roberto Bagnacci, Giulio di Meglio, Nunzia Mazzei, M. Antonietta Barucci, Andrea Inst Informat Sci & Technol ISTI CNR Pisa Italy Univ Pisa Dept Informat Engn Pisa Italy Univ Pisa Dept Translat Res & New Surg & Med Technol Pisa Italy Azienda Osped Univ Careggi Florence Italy Azienda USL Toscana Ctr Florence Italy Azienda Osped Univ Senese Siena Italy Inst Appl Phys IFAC CNR Florence Italy

ISBN: (数字)9781665487917

ISBN: (纸本)9781665487917

Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.

关键词： data models entity-relationship diagram imaging bio-banks

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Embedded Intelligence for Safety and Security Machine Vision Applications 21st

Embedded Intelligence for Safety and Security Machine Vision...

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21st international conference on Image Analysis and processing (ICIAP)

作者： Lioupis, Panagiotis Dadoukis, Aris Maltezos, Evangelos Karagiannidis, Lazaros Amditis, Angelos Gonzalez, Maite Martin, Jon Cantero, David Larranaga, Mikel Inst Commun & Comp Syst ICCS Zografos 15773 Greece Fdn Tekniker Inaki Goenaga 5 Eibar 20600 Spain

ISBN: (纸本)9783031133244;9783031133237

Artificial intelligence (AI) has experienced a recent increase in use across a wide variety of domains, such as image processing for security applications. Deep learning, a subset of AI, is particularly useful for those image processing applications. Deep learning methods can achieve state-of-the-art results on computer vision for image classification, object detection, and face recognition applications. This allows to automate video surveillance reducing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Hardware manufacturers have developed powerful co-processors specifically designed to execute deep learning algorithms. But also, new lightweight open-source middleware for constrained resources devices such as EdgeX foundry have emerged to facilitate the collection and processing of data at sensor level, with communication capabilities to cloud enterprise applications. The aim of this work is to show and describe the development of Smart Camera Systems within S4AllCities H2020 project, following the edge approach.

关键词： Horizon2020 Edge EdgeX foundry Machine vision Artificial intelligence Deep learning

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Resource Binding and Module Placement algorithms for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare 18th

Resource Binding and Module Placement Algorithms for Conti...

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18th international conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Yang, Zhongliao Huang, Huichang Liu, Zeyi Dong, Chen Xu, Li College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

Continuous-Flow Microfluidic Biochip (CFMB), with their integrated features, bring traditional biochemical experiments on a single chip to accomplish complex operations and reactions through precise control, efficient reactions and emerging ways of saving reagents. In the field of intelligent digital healthcare, CFMB have attracted a lot of attention. However, traditional manual design schemes can no longer meet the needs of increasingly complex chip architecture design. Therefore, this paper proposes an automated design method for resource binding and module placement of CFMB based on a list scheduling algorithm and an improved Simulated Annealing algorithm. Through the resource binding and scheduling design based on the list scheduling algorithm, an effective scheduling strategy is generated, which effectively improves the biochip execution efficiency. In addition, the improved Simulated Annealing algorithm solves the module placement problem in the biochip in a limited physical space. Compared with some benchmark algorithms, the experimental results demonstrate the effectiveness of the method in the biochip design process and provide a practical framework for further development of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Biochips

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YouTube Sentiment Analysis on Healthcare Product Campaigns: Combining Lexicons and Machine Learning models 14

YouTube Sentiment Analysis on Healthcare Product Campaigns: ...

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14th international conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Anastasiou, Panagiota Tzafilkou, Katerina Karapiperis, Dimitrios Tjortjis, Christos School of Sciences and Technology International Hellenic University Thessaloniki Greece

ISBN: (纸本)9798350318067

YouTube comments provide a rich source of data to classify consumers' opinions and emotions towards particular products, brands, and social or health related topics. Previous studies applied either lexicon based or machine learning approaches to classify sentiment through social media comments. This study employs a hybrid strategy that combines machine learning and lexicon-based methods, to classify consumers' sentiment towards health care products promoted through social marketing campaigns in YouTube channels. A total of 59,695 records were exported through the YouTube API, from 18 relevant video campaigns. The comments were labeled using general-purpose sentiment lexicons like TextBlob, VADER and Flair. It was found that 66.4 % of the comments were similarly classified from TextBlob and VADER. However, further analysis revealed that TextBlob generated slightly more accurate results than VADER in the differentiated outcomes. A set of machine learning models were selected to classify the sentiments of the comments, including Logistic Regression (LR), Multinomial Naïve Bayes (***), Random Forest (RF), Support Vector Machine (SVM), and Stochastic Gradient Descent Classifier (SGD Classifier). Accuracy, precision, recall, and F1-score were calculated to assess the performance of the algorithms. The most accurate scores were generated from SVM and SGD (accuracy = 91 %, F1-score = 0.89), followed by LR (accuracy = 90%, F1-score = 0.88) and RF (accuracy = 87%, F1-score = 0.84). Implications and limitations are discussed in the paper. © 2023 IEEE.

关键词： Sentiment analysis

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Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare 18th

Genetic-A* Algorithm-Based Routing for Continuous-Flow Micr...

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18th international conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Huang, Huichang Yang, Zhongliao Zhong, Jiayuan Xu, Li Dong, Chen Bao, Ruishen College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

In the field of intelligent digital healthcare, Continuous-flow microfluidic biochip (CFMB) has become a research direction of widespread concern. CFMB integrates a large number of microvalves and large-scale microchannel networks into a single chip, enabling efficient execution of various biochemical protocols. However, as the scale of the chip increases, the routing task for CFMB becomes increasingly complex, and traditional manual routing is no longer sufficient to meet the requirements. Therefore, this paper proposes an automatic routing framework for CFMB based on Genetic algorithm (GA) and A* algorithms. Specifically, we adopt a two-stage A* algorithm to design the routing between modules, using the routing results obtained from the A* algorithm as the basis for evaluating the quality of solutions in the GA algorithm. Then, the GA algorithm is used to search for the optimal approximate solution in the solution space. Experimental results show that this method can reduce routing length and minimize routing crossings, thereby improving the parallel transmission speed of reagents on CFMB. This approach provides a feasible solution for large-scale automated routing of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic algorithms

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