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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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Confidence Estimation Based on the Explanations of Model's Predictions

Confidence Estimation Based on the Explanations of Model's P...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Wu, Kaiyue Huang, Changwu Yao, Xin Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China

ISBN: (纸本)9798350316889

Estimating the confidence of a machine learning (ML) model plays an important role in minimizing undesirable outcomes and safety risks when using ML models in the real world, especially in high-stake application scenarios. However, the majority of existing confidence estimation methods do not work well without incorporating internal information from the model, such as the gradients, internal representations or checkpoints of its training process, which might compromise the security of the model. To alleviate such limitations, we intend to design a reliable confidence estimation method that requires as little internal model information as possible, yet precisely detecting uncertain samples for the model. In this work, we propose a confidence estimation approach that learns the confidence criterion (that is, the true class probability, TCP) based on the explanations of model's predictions. The proposed approach utilizes the knowledge learned in the model with the leverage of the explanations for model's prediction rather than directly extracting internal information of the model, so that it can be adopted in a wide range of applications. Several experiments, including experiments on different datasets, different models and non-IID (independent and identically distributed) test datasets, are conducted to examine the effectiveness of our confidence estimation method. The experimental results show that our proposed approach provides a reliable confidence estimation and generally performs better than the compared methods, especially in the capability of recognizing when a ML model makes errors. © 2023 IEEE.

关键词： Machine learning

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Probabilistic Device Scheduling for Over-the-Air Federated Learning 23

Probabilistic Device Scheduling for Over-the-Air Federated L...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Sun, Yuchang Lin, Zehong Mao, Yuyi Jin, Shi Zhang, Jun The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Southeast University National Mobile Communications Research Laboratory Nanjing210096 China

ISBN: (纸本)9798350325959

Federated learning (FL) is an emerging distributed training scheme where edge devices collaboratively train a model by uploading model updates instead of private data. To address the communication bottleneck, over-the-air (OTA) computation has been introduced to FL, which allows multiple edge devices to upload their gradient updates concurrently for aggregation. However, OTA computation is plagued by the communication error, which is critically affected by the device selection policy and impacts the performance of the output model. In this paper, we propose a probabilistic device selection scheme PO-FL, which effectively enhances the convergence performance of over-the-air FL. Specifically, each device is selected for OTA computation according to the predetermined probability, and its local update is scaled by this probability. By analyzing the convergence of PO-FL, we show that its convergence is determined by the device selection via the communication error and the variance of global update. Then, we propose a device selection algorithm that jointly considers the channel condition and gradient update importance of edge devices to optimize their selection probabilities. The experimental results on the MNIST dataset demonstrate that the proposed algorithm converges faster and learns better models than the baselines. © 2023 IEEE.

关键词： channel awareness device scheduling Federated learning (FL) gradient importance over-the-air computation (AirComp)

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A new online field feature selection algorithm based on streaming data

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Potential detection of Flavescence dorée in the vineyard using close-range hyperspectral imaging

Potential detection of Flavescence dorée in the vineyard us...

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2023 International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Barjaktarovic, Marko Santoni, Massimo Faralli, Michele Bertamini, Massimo Bruzzone, Lorenzo School of Electrical Engineering University of Belgrade Serbia Remote Sensing Laboratory University of Trento Italy Remote Sensing Laboratory Department of Information Engineering and Computer Science University of Trento Italy University of Trento Italy Research and Innovation Centre Fondazione Edmund Mach Italy

ISBN: (纸本)9798350322972

Grapevine is one of the most important crops cultivated across Europe. Climate factors and diseases constantly threaten its production. Recently, Flavescence dorée (FD), an incurable grapevine disease with the obligation to uproot each infected plant, has been widely spread in Europe. The symptoms of FD are visually expressed in the late summer. Currently, the adopted procedure consists in scouting for infected plants by trained experts, which is time-consuming and not frequent enough. As stress development causes subtle spectral changes before any visible symptoms appear, during the summer of 2022, hyperspectral and multispectral images were acquired in the two vineyards near Riva del Garda, Trentino, Italy. A classification accuracy between 90.2 % and 96.9 % in distinguishing between infected and healthy plants was obtained from the hyperspectral data. These findings justify further efforts to use an in-house developed, affordable multispectral camera, significantly reducing equipment cost and procedure complexity while mapping the relevant spectral channels. © 2023 IEEE.

关键词： Hyperspectral imaging

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Modelling the Effect of the DC Link Decoupling Capacitor of a Commutation Power Loop Using a Thevenin-Based Frequency Domain Approach

Modelling the Effect of the DC Link Decoupling Capacitor of ...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Ayooluwa Ajiboye Ayodhya Somiruwan Gamwari Rakesh Resalayyan Alireza Khaligh Department of Electrical and Computer Engineering Maryland Power Electronics Laboratory The Institute for Systems Research University of Maryland College Park USA

The high commutation speed of wide band-gap devices causes undesired voltage overshoots and oscillatory behavior observed across the drain-source port. The mitigation of these voltage overshoots and oscillations caused by the resonant interaction between the commutation loop parasitic inductance and the switch parasitic output capacitance requires additional design considerations. In this work, a Thevenin-based frequency domain approach for modelling the switch voltage overshoot and oscillations with respect to the commutation power loop is proposed. The switching transition and its associated slew rate is modelled using a clamped-ramp function. The two figures of merit used to quantify the effect of the circuit elements in the commutation power loop with respect to the resonant oscillations are the voltage overshoot and time to steady state. Using the clamped-ramp function and the two figures of merit, the effect of the parasitic inductance and the decoupling capacitor on the power loop is analyzed. The trends observed from the analyses are used to obtain recommendations regarding the sizing and placement of the decoupling capacitor in the power loop. The proposed Thevenin-based frequency domain model is experimentally validated by comparing the analytical results with both the LTspice simulations and the experimental waveforms for a SiC-based 400 V, 20 A double pulse test.

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Thermal energy dependent transient permittivity of epsilon-near-zero material

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science China(Physics,Mechanics & Astronomy) 2022年第8期65卷 96-101页

作者： Heng Wang Lixun Sun Kang Du Wending Zhang SooJin Chua Guixin Li Ting Mei MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi’an710129China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen518055China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen518055China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore LEES Program Singapore-MIT Alliance for Research&Technology(SMART)Singapore138602Singapore

Transparent conductive oxides exhibit attractive optical nonlinearity with ultrafast response and giant refractive index change near the epsilon-near-zero(ENZ) wavelength, originating from the intraband dynamics of conduction electrons. The optical nonlinearity of ENZ materials has been explained by using the overall-effective-mass and the overall-scattering-time of electrons in the extended Drude model. However, their response to optical excitation is yet the last building block to complete the theory. In this paper, the concept of thermal energy is theoretically proposed to account for the total energy of conduction electrons exceeding their thermal equilibrium value. The time-varying thermal energy is adopted to describe the transient optical response of indium-tin-oxide(ITO), a typical ENZ material. A spectrally-resolved femtosecond pump-probe experiment was conducted to verify our theory. By correlating the thermal energy with the pumping density, both the giant change and the transient response of the permittivity of ITO can be predicted. The results in this work provide a new methodology to describe the transient permittivities of ENZ materials, which will benefit the design of ENZ-based nonlinear photonic devices.

关键词： epsilon-near-zero optical nonlinearity transient response thermal energy hot electron indium tin oxide

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Abstract: A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT

Abstract: A Spatiotemporal Model for Precise and Efficient F...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Ploner, Stefan B. Chen, Siyu Won, Jungeun Husvogt, Lennart Breininger, Katharina Schottenhamml, Julia Fujimoto, James G. Maier, Andreas K. Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Electrical Engineering and Computer Science Research Laboratory for Electronics Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9783658416560

Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology. OCT instruments image by raster-scanning a focused light spot across the retina, acquiring sequential cross-sectional images to generate volumetric data. Patient eye motion during the acquisition poses unique challenges: Non-rigid, discontinuous distortions can occur, leading to gaps in data and distorted topographic measurements. We present a new distortion model and a corresponding fully-automatic, reference-free optimization strategy for computational motion correction in orthogonally raster-scanned, retinal OCT volumes. Using a novel, domain-specific spatiotemporal parametrization of forward-warping displacements, eye motion can be corrected continuously for the first time. Parameter estimation with temporal regularization improves robustness and accuracy over previous spatial approaches. We correct each A-scan individually in 3D in a single mapping, including repeated acquisitions used in OCT angiography protocols. Specialized 3D forward image warping reduces median runtime to © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： Optical tomography

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