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OptoElectronics and Communications Conference, OECC

作者： Yuanbin Liu Liangjun Lu Jianping Chen Linjie Zhou Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu China

We demonstrate 4-channel tunable optical delay lines with delay $\gt500$ ps and delay error $\lt \pm 1$ ps. The delay loss is 3 dB/ns and the delay deviation is less than 1 ps among 4 channels. © 2021 The Author(s)

ISBN: (数字)9781943580927

ISBN: (纸本)9781665400060

关键词： Optical losses Phased arrays Integrated optics Insertion loss Delay lines Adaptive optics Silicon

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Optimization-driven Hierarchical Deep Reinforcement Learning for Hybrid Relaying Communications

Optimization-driven Hierarchical Deep Reinforcement Learning...

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IEEE Conference on Wireless Communications and Networking

作者： Yuze Zou Yutong Xie Canhui Zhang Shimin Gong Dinh Thai Hoang Dusit Niyato School of Intelligent Systems Engineering Sun Yat-sen University China School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

In this paper, we employ multiple wireless-powered user devices as wireless relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. To improve energy efficiency, we design a hybrid relaying communication strategy in which wireless relays are allowed to operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall SNR by jointly optimizing the access point's beamforming strategy as well as individual relays' radio modes and operating parameters. Due to the non-convex and combinatorial structure of the SNR maximization problem, we develop a deep reinforcement learning approach that adapts the beamforming and relaying strategies dynamically. In particular, we propose a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach that integrates the model-based optimization into the framework of conventional DDPG approach. It decomposes the discrete relay mode selection into the outer-loop by using deep Q-network (DQN) algorithm and then optimizes the continuous beamforming and relays' operating parameters by using the inner-loop DDPG algorithm. Simulation results reveal that the H-DDPG is robust to the hyper parameters and can speed up the learning process compared to the conventional DDPG approach.

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On the Age of Status Updates in Unreliable Multi-Source M/G/1 Queueing systems

arXiv

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arXiv 2022年

作者： SenthilKumar, Muthukrishnan Dadlani, Aresh Moradian, Masoumeh Khonsari, Ahmad Tsiftsis, Theodoros A. The Department of Applied Mathematics and Computational Sciences Psg College of Technology Coimbatore641004 India The Faculty of Arts - Interdisciplinary Studies University of Alberta EdmontonABT6G 2E7 Canada The Department of Electrical and Computer Engineering Nazarbayev University Astana010000 Kazakhstan The School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran The Department of Electrical and Computer Engineering University of Tehran Iran The Institute for Research in Fundamental Sciences Tehran Iran The School of Intelligent Systems Science and Engineering Jinan University Zhuhai Campus Zhuhai519070 China

The timeliness of status message delivery in communications networks is subjective to time-varying wireless channel transmissions. In this paper, we investigate the age of information (AoI) of each source in a multi-source M/G/1queueingupdatesystem with active server failures. In particular, we adopt the method of supplementary variables to derive a closed-form expression for the average AoI in terms of system parameters, where the server repair time follows a general distribution and the service time of packets generated by independent sources is a general random variable. Numerical results are provided to validate the effectiveness of the proposed packet serving policy under different parametric settings. © 2022, CC BY.

关键词： Real time systems

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Detection of effective genes in colon cancer: A machine learning approach

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Informatics in Medicine Unlocked 2021年 24卷

作者： Fahami, Mohammad Amin Roshanzamir, Mohamad Izadi, Navid Hoseini Keyvani, Vahideh Alizadehsani, Roohallah Department of Electrical and Computer Engineering Isfahan University of Technology 84156-83111 Isfahan Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa 74617-81189 Iran Department of Biology Faculty of Science Shahid Chamran University of Ahvaz Ahvaz Iran Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Australia

Nowadays, a variety of cancers have become common among humans which unfortunately are the cause of death for many of these people. Early detection and diagnosis of cancers can have a significant impact on the survival of patients and treatment cost reduction. Colon cancer is the third and the second main cause of women's and men's death worldwide among cancers. Hence, many researchers have been trying to provide new methods for early diagnosis of colon cancer. In this study, we apply statistical hypothesis tests such as t-test and Mann–Whitney–Wilcoxon and machine learning methods such as Neural Network, KNN and Decision Tree to detect the most effective genes in the vital status of colon cancer patients. We normalize the dataset using a new two-step method. In the first step, the genes within each sample (patient) are normalized to have zero mean and unit variance. In the second step, normalization is done for each gene across the whole dataset. Analyzing the results shows that this normalization method is more efficient than the others and improves the overall performance of the research. Afterwards, we apply unsupervised learning methods to find the meaningful structures in colon cancer gene expressions. In this regard, the dimensionality of the dataset is reduced by employing Principle Component Analysis (PCA). Next, we cluster the patients according to the PCA extracted features. We then check the labeling results of unsupervised learning methods using different supervised learning algorithms. Finally, we determine genes which have major impact on colon cancer mortality rate in each cluster. Our conducted study is the first which suggests that the colon cancer patients can be categorized into two clusters. In each cluster, 20 effective genes were extracted which can be important for early diagnosis of colon cancer. Many of these genes have been identified for the first time. © 2021 The Authors

关键词： Colon cancer Dimension reduction Machine learning Statistical hypothesis tests Unsupervised learning

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Myocarditis Diagnosis: A Method using Mutual Learning-Based ABC and Reinforcement Learning

Myocarditis Diagnosis: A Method using Mutual Learning-Based ...

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International Symposium on Computational Intelligence and Informatics

作者： Saba Danaei Arsam Bostani Seyed Vahid Moravvej Fardin Mohammadi Roohallah Alizadehsani Afshin Shoeibi Hamid Alinejad-Rokny Saeid Nahavandi Adiban Institute of Higher Education Semnan Iran Department of mechanical engineering of biosystems Urmia university Department of exercise physiology & health science University of tehran Internship in UNSW BioMedical Machine Learning Lab Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Waurn Ponds Victoria Australia UNSW Data Science Hub The University of New South Wales (UNSW Sydney) Sydney New South Wales Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia

ISBN: (纸本)9798350398830

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body’s immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Mechatronics Computational modeling Metaheuristics Reinforcement learning Myocardium Markov processes Classification algorithms

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The Interrelationships between the length of stay, readmission, and post-acute care referral in cardiac surgery patients

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Healthcare Analytics 2022年 2卷 100062-100062页

作者： Sultana, Ineen Erraguntla, Madhav Kum, Hye-Chung Delen, Dursun Lawley, Mark Department of Industrial and Systems Engineering Texas A&M University College Station TX United States Data Scientist WVU Research Corporation West Virginia University United States School of Public Health Texas A&M University College Station TX United States Department of Management Science and Information Systems Spears School of Business Oklahoma State University United States Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

Prolonged hospital stays, and readmission contribute to substantial healthcare cost. Hence, an assessment of the optimal inpatient length of stay (LOS) associated with lower readmission rate is important for healthcare providers. Post-acute care (PAC) facilities have promising potential to shorten the LOS;however, currently their influence on overall patient outcomes is not well understood. The primary goal of this study is to highlight the interrelated risk factors of LOS and readmission for cardiac patients. The study also examines the influence of PAC referral on LOS and readmission. In this paper, a cohort of 13,982 Coronary Artery Bypass Graft (CABG) and Valve Replacement (VR) patients from 49 hospitals in the U.S. were analyzed with respect to the association of healthcare delivery, demographics, PAC referral, and clinical conditions with LOS and readmission. A generalized linear mixed model and multinomial logistic regression model were developed to evaluate the readmission and LOS associative risk factors, respectively. Referral to PAC was included as a vital predictor in both models to examine its impact on optimal LOS and improving patient outcomes. The results indicate a non-uniform care distribution across census divisions and an inverse relationship between LOS and readmissions. The analytics showed that higher LOS patients were more often referred to PAC and yet they were more prone to readmission except for the patients who were referred to Long Term Care (LTC). This study identifies the effects of healthcare delivery, demographic, comorbidity, and PAC referral factors on readmission and LOS, so that personalized acute and post-acute care coordination can be achieved to improve overall patient outcomes. Interventions such as treating CABG and VR patients with multiple comorbidities for longer time in acute hospitals and increasing LTC referral can result in significant improvement. © 2022 The Author(s)

关键词： Cardiac surgery Length of stay Logistic regression Post-acute care Readmission

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Blister Package Classification Using ResNet-101 for Identification of Medication

Blister Package Classification Using ResNet-101 for Identifi...

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International Computer science and engineering Conference (ICSEC)

作者： Narit Hnoohom Nagorn Maitrichit Pitchaya Chotivatunyu Virach Sornlertlamvanich Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Mahidol University Nakorn Pathom Thailand Faculty of Data Science Asia AI Institute (AAII) Musashino University Tokyo Japan Faculty of Engineering Thammasat University Thailand Pathumthani Thailand School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9781665411981

This research aimed to fine-tune image classification with deep learning techniques to verify the dispensing of prescriptions in hospitals. The proposed approach will be able to help pharmacies reduce the errors that lead to patients receiving the wrong medications. The image classification model uses a double-side transformed image dataset with download from Highlighted Deep Learning (HDL) paper. The dataset collected two-hundred seventy-two images for types of medicine blister packs, including 72 images of front-side and backside merged with a horizontal cropped background, which were used for training the model. The blister package image dataset uses a deep learning model with a ResNet-101 pre-trained model from the TensorFlow framework. The experimental results indicated that the TensorFlow framework achieved higher precision, recall, and F1-score than the Caffe framework. A ResNet-101 model with histogram equalization in the front and backside has the highest accuracy at 100 percent.

关键词： Deep learning Training Computer science Histograms Hospitals Image processing Computational modeling

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Cosmological phase-space analysis of f(G)-theories of gravity

arXiv

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arXiv 2025年

作者： Papagiannopoulos, Giannis Luongo, Orlando Leon, Genly Paliathanasis, Andronikos Department of Physics National & Kapodistrian University of Athens Zografou Campus GR 157 73 Athens Greece School of Science and Technology University of Camerino Via Madonna delle Carceri Camerino62032 Italy Department of Nanoscale Science and Engineering University at Albany-SUNY AlbanyNY12222 United States INAF - Osservatorio Astronomico di Brera Milano Italy Sezione di Perugia Perugia06123 Italy Al-Farabi Kazakh National University Al-Farabi av. 71 Almaty050040 Kazakhstan Departamento de Matemáticas Universidad Católica del Norte Avda. Angamos 0610 Casilla Antofagasta1280 Chile Institute of Systems Science Department of Mathematics Faculty of Applied Sciences Durban University of Technology Durban4000 South Africa School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch University Stellenbosch7602 South Africa South Africa

The impact of topological terms that modify the Hilbert-Einstein action is here explored by virtue of a further f(G) contribution. In particular, we investigate the phase-space stability and critical points of an equivalent scalar field representation that makes use of a massive field, whose potential is function of the topological correction. To do so, we introduce to the gravitational Action Integral a Lagrange multiplier and model the modified Friedmann equations by virtue of new non-dimensional variables. We single out dimensionless variables that permit a priori the Hubble rate change of sign, enabling regions in which the Hubble parameter either vanishes or becomes negative. In this respect, we thus analyze the various possibilities associated with a universe characterized by such topological contributions and find the attractors, saddle points and regions of stability, in general. The overall analysis is carried out considering the exponential potential first and then shifting to more complicated cases, where the underlying variables do not simplify. We compute the eigenvalues of our coupled differential equations and, accordingly, the stability of the system, in both a spatially-flat and non-flat universe. Quite remarkably, regardless of the spatial curvature, we show that a stable de Sitter-like phase that can model current time appears only a small fraction of the entire phase-space, suggesting that the model under exam is unlikely in describing the whole universe dynamics, i.e., the topological terms appear disfavored in framing the entire evolution of the universe. © 2025, CC BY.

关键词： Topology

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Learning enables adaptation in cooperation for multi-player stochastic games

arXiv

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arXiv 2020年

作者： Huang, Feng Cao, Ming Wang, Long Center for Systems and Control College of Engineering Peking University Beijing100871 China Center for Data Science and System Complexity Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands

Interactions among individuals in natural populations often occur in a dynamically changing environment. Understanding the role of environmental variation in population dynamics has long been a central topic in theoretical ecology and population biology. However, the key question of how individuals, in the middle of challenging social dilemmas (e.g., the "tragedy of the commons"), modulate their behaviors to adapt to the fluctuation of the environment has not yet been addressed satisfactorily. Utilizing evolutionary game theory and stochastic games, we develop a game-theoretical framework that incorporates the adaptive mechanism of reinforcement learning to investigate whether cooperative behaviors can evolve in the ever-changing group interaction environment. When the action choices of players are just slightly influenced by past reinforcements, we construct an analytical condition to determine whether cooperation can be favored over defection. Intuitively, this condition reveals why and how the environment can mediate cooperative dilemmas. Under our model architecture, we also compare this learning mechanism with two non-learning decision rules, and we find that learning significantly improves the propensity for cooperation in weak social dilemmas, and, in sharp contrast, hinders cooperation in strong social dilemmas. Our results suggest that in complex social-ecological dilemmas, learning enables the adaptation of individuals to varying environments. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

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CBLNER: A Multi-models Biomedical Named Entity Recognition System Based on Machine Learning 1

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15th International Conference on intelligent Computing, ICIC 2019

作者： Lejun, Gong Xiaolin, Liu Xuemin, Yang Lipeng, Zhang Yao, Jia Ronggen, Yang Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China

ISBN: (数字)9783030269692

ISBN: (纸本)9783030269685

Biomedical named entities is fundamental recognition task in biomedical text mining. This paper developed a system for identifying biomedical entities with four models including CRF, LSTM, Bi-LSTM and BiLSTM-CRF. The system achieved the following performance in test data Genia V3.02: CRF with an F score of 75.91%, LSTM with an F score of 71.69%, BiLSTM with a F score of 74.37%, BiLSTM-CRF with a F score of 76.81%. Experimental results show the performance of BiLSTM-CRF model is better than other three models. Compared with CRF model, Bi-LSTM-CRF model has better recognition effect for biological entities in long text and entities that modified by modifiers. Therefore, CBLNER system lays a foundation for further relationship and event extraction, and could also provide reference for entity recognition research in other fields. © 2019, Springer Nature Switzerland AG.

关键词： Character recognition

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