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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7157 条记录，以下是681-690 订阅

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EEG-Eye Movements Cross-Modal Decision Confidence Measurement with Generative Adversarial Networks 11

EEG-Eye Movements Cross-Modal Decision Confidence Measuremen...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Fei, Cheng Li, Rui Zhao, Li-Ming Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong Univ Key Lab Shanghai Educ Commiss Intelligent Interac Dept Comp Sci & Engn Ctr Brain Like Comp & Machine Intelligence 800 Dongchuan Rd Shanghai Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr 800 Dongchuan Rd Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Med RuiJin Hosp RuiJin Mihoyo LabClin Neurosci Ctr 197 Ruijin 2nd Rd Shanghai 200020 Peoples R China

ISBN: (纸本)9781665462921

Decision confidence is an individual's feeling of correctness or optimization when making a decision. Various physiological signals, including electroencephalography (EEG) and eye movements have been studied extensively in measuring levels of decision confidence in humans. While multimodal fusion generally performs better than single-modal approaches, it requires data from different modalities at a greater cost. In particular, collection of EEG data is more complicated and time consuming while eye movement signals are much easier to acquire. To tackle this problem, we propose a crossmodal method based on generative adversarial learning. In our method, the intrinsic relationship between eye movement and EEG features in a high-level feature space can be learned in the training phase, and then we can obtain multimodal information during the test phase when only eye movements are available as inputs. Experimental results on the SEED-VPDC dataset demonstrate that our proposed method outperforms singlemodal methods trained and tested only on eye movement signals with an improvement of approximately 5.43% in accuracy, and maintains competitive performance in comparison with multimodal methods. Our cross-modal approach requires only eye movements as inputs and reduces reliance on EEG data, making the decision confidence measurement more applicable and practicable.

关键词： Eye movements

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BlockDeepNet: A Proposed Framework for the Detection of CT-MRI Imaging Using Blockchain and Deep learning Architecture 11th

BlockDeepNet: A Proposed Framework for the Detection of CT-M...

引用

11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Dudeja, Tina Dubey, Sanjay Kumar Bhatt, Ashutosh Kumar Amity University Uttar Pradesh Greater Noida India Uttarakhand Open University Uttarakhand Haldwani India

ISBN: (纸本)9789819717231

Early diagnosis of brain tumors can control the severity of its spread in the affected area. Numerous deep learning models have been proposed for CT-MRI imaging which act as tool for better data visualization and also automate the diagnosis procedure. Along with that the real-world problem is security while sharing the data among hospitals or remote diagnosis of patients by medical practitioners and researchers. the most important concern in sharing medical data is privacy and to achieve this goal, various collaborative models have been developed to secure data transfer. through this paper, a proposed collaborative model will be utilized constitutes deep learning model and blockchain mechanism which will collect MRI images from different sources and then training to that data will be provided through a global learning model. By using the mechanism of blockchain technology, the data will be authenticated and in next phase training on globally shared data will be provided via keeping the concern of privacy. Afterward, a deep federated learning model will be implemented for network-based segmentation and classification for multiple classes of the tumor for the improved and secure scenario. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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SELF-ENSEMBLE PROTECTION: TRAINING CHECKPOINTS ARE GOOD data PROTECTORS 11

SELF-ENSEMBLE PROTECTION: TRAINING CHECKPOINTS ARE GOOD DATA...

引用

11th international conference on learning Representations, ICLR 2023

作者： Chen, Sizhe Yuan, Geng Cheng, Xinwen Gong, Yifan Qin, Minghai Wang, Yanzhi Huang, Xiaolin Department of Automation Shanghai Jiao Tong University China Department of Electrical and Computer Engineering Northeastern University United States

As data becomes increasingly vital, a company would be very cautious about releasing data, because the competitors could use it to train high-performance models, thereby posing a tremendous threat to the company's commercial competence. To prevent training good models on the data, we could add imperceptible perturbations to it. Since such perturbations aim at hurting the entire training process, they should reflect the vulnerability of DNN training, rather than that of a single model. Based on this new idea, we seek perturbed examples that are always unrecognized (never correctly classified) in training. In this paper, we uncover them by model checkpoints' gradients, forming the proposed self-ensemble protection (SEP), which is very effective because (1) learning on examples ignored during normal training tends to yield DNNs ignoring normal examples;(2) checkpoints' cross-model gradients are close to orthogonal, meaning that they are as diverse as DNNs with different architectures. that is, our amazing performance of ensemble only requires the computation of training one model. By extensive experiments with 9 baselines on 3 datasets and 5 architectures, SEP is verified to be a new state-of-the-art, e.g., our small ∞ = 2/255 perturbations reduce the accuracy of a CIFAR-10 ResNet18 from 94.56% to 14.68%, compared to 41.35% by the best-known method. Code is available at https://***/Sizhe-Chen/SEP. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

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Towards AI-controlled FES-restoration of arm movements: Controlling for progressive muscular fatigue with Gaussian state-space models 11

Towards AI-controlled FES-restoration of arm movements: Cont...

引用

11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Wannawas, Nat Faisal, A. Aldo Imperial Coll London Brain & Behav Lab London SW7 2AZ England Univ Bayreuth Chair Digital Hlth & Data Sci Bayreuth Germany

ISBN: (纸本)9781665462921

Reaching disability limits an individual's ability in performing daily tasks. Surface Functional Electrical Stimulation (FES) offers a non-invasive solution to restore the lost abilities. However, inducing desired movements using FES is still an open engineering problem. this problem is accentuated by the complexities of human arms' neuromechanics and the variations across individuals. Reinforcement learning (RL) emerges as a promising approach to govern customised control rules for different subjects and settings. Yet, one remaining challenge of using RL to control FES is unobservable muscle fatigue that progressively changes as an unknown function of the stimulation, breaking the Markovian assumption of RL. In this work, we present a method to address the unobservable muscle fatigue issue, allowing our RL controller to achieve higher control performances. Our method is based on a Gaussian StateSpace Model (GSSM) that utilizes recurrent neural networks to learn Markovian state-spaces from partial observations. the GSSM is used as a filter that converts the observations into the state-space representation for RL to preserve the Markovian assumption. Here, we start with presenting the modification of the original GSSM to address an overconfident issue. We then present the interaction between RL and the modified GSSM, followed by the setup for FES control learning. We test our RLGSSM system on a planar reaching setting in simulation using a detailed neuromechanical model and show that the GSSM can help RL maintain its control performance against the fatigue.

关键词： Functional Electrical Stimulation FES Gaussian State-Space Model Reinforcement learning Arm Motions

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OFFLINE REINFORCEMENT learning WIth DIFFERENTIABLE FUNCTION APPROXIMATION IS PROVABLY EFFICIENT 11

OFFLINE REINFORCEMENT LEARNING WITH DIFFERENTIABLE FUNCTION ...

引用

11th international conference on learning Representations, ICLR 2023

作者： Yin, Ming Wang, Mengdi Wang, Yu-Xiang Department of Computer Science University of California Santa Barbara United States Department of Electrical and Computer Engineering Princeton University United States

Offline reinforcement learning, which aims at optimizing sequential decision-making strategies with historical data, has been extensively applied in real-life ***-Of-the-Art algorithms usually leverage powerful function approximators (*** networks) to alleviate the sample complexity hurdle for better empirical *** the successes, a more systematic understanding of the statistical complexity for function approximation remains *** bridging the gap, we take a step by considering offline reinforcement learning with differentiable function class approximation (DFA).this function class naturally incorporates a wide range of models with nonlinear/nonconvex *** show offline RL with differentiable function approximation is provably efficient by analyzing the pessimistic fitted Q-learning (PFQL) algorithm, and our results provide the theoretical basis for understanding a variety of practical heuristics that rely on Fitted Q-Iteration style *** addition, we further improve our guarantee with a tighter instance-dependent *** hope our work could draw interest in studying reinforcement learning with differentiable function approximation beyond the scope of current research. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Reinforcement learning

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Application Research of On-Board Satellite Remote Sensing Image Terrain Classification Based on Deep learning 11th

Application Research of On-Board Satellite Remote Sensing Im...

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11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Tian, Hexiang Zhang, Jingya Zhao, Wenrui Institute of Remote Sensing Satellite CAST Beijing100094 China

ISBN: (纸本)9789819721238

Aiming at the real-time on-board intelligent decoding/translating need of the satellite remote sensing image, the article raises up a system structure of satellite remote sensing image terrain classification based on the combination of terrain servers and on-board AI analyzing platform, and designs an on-board satellite remote sensing image terrain classification algorithm based on deepabv3+. then, the optimizing solution satisfies the constraints of the performance and the storage of the on-board intelligent information system is offered. Tested by on-board AI analyzing platform built on land, the solution is proved to be able to satisfy the on-board real-time processing need, the on-board image decoding/translating speed and accuracy requirements. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Satellites

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Revolutionizing Water Level Monitoring with the Wi-Fi Board 18

Revolutionizing Water Level Monitoring with the Wi-Fi Board

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Dogra, Ayush Badhoutiya, Arti Goyal, Bhawna Kriplani, Khushbu Juyal, Ashima Kaur, Harpreet Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Gla University Dept. of Electrical Engineering Mathura India University Center for Research and Development Chandigarh University Punjab India Ies Institute of Technology and Management Ies University Department of Cse Bhopal India Uttaranchal Institute of Technology Uttaranchal University Dept. of Electronics & Communication Engineering Dehradun India Gulzar Group of Institutions Dept. of Cse Punjab India

ISBN: (纸本)9789380544519

In this study, we propose to use NuttyFi ESP8266 WiFi board to measure water level in an inventive way that combines creativity and state-of-the-art technology. We further propose simple connectivity and real-time data transfer using the ESP8266 NuttyFi WiFi board, allowing customers to check the water level from any position on the planet. this system also incorporates an actuator module that automatically regulates the motor according level of water. thus, the pump will automatically shut down by minimizing energy waste and preventing waste when there is sufficient water. the method monitors and regulate the water tank without being physical presence by using NuttyFi ESP8266 WiFi board. Utilizing a smartphone or tablet, users may easily get water level data and oversight choices thanks to the implementation of the Blynk IoT app. Additionally, the precision and dependability of the readings of the water level are improved by the use of ultrasonic technology. In the end, the proposed system proposes to revolutionize the monitoring of water levels by combining state-of-the-art technology, wireless communication, and intelligent control. It proposes an effective and easy to use solution for optimal water management by supplying customers with immediate time information and automatic pump control. When compared to recent state-of-the-art systems, our system shows efficient and appropriate performance with less computational cost. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： ESP8266 Nuttyfi wifi board innovative Lora RF network research technology water level monitoring

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6th Iraqi international conference on engineering Technology and its Applications, IICETA 2023

6th Iraqi International Conference on Engineering Technology...

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6th Iraqi international conference on engineering Technology and its Applications, IICETA 2023

ISBN: (纸本)9798350303339

the proceedings contain 148 papers. the topics discussed include: intelligent humming bird optimization based distance aware routing scheme for vehicular networks;image dataset for cervical cell diagnosis – a review;AI-based risk monitoring in COVID cases after lockdown;automated modulation classification of communication signals using hybrid deep learning;leveraging enhanced bald eagle search optimization for energy-aware clustering in wireless sensor networks;decision making strategy for COVID-19 risk minimization using data-driven model;improved qubit neural network based computer aided detection model for COVID-19 on chest radiographs;IoT system of medical equipment monitoring and ambulance tracking;energy based stable multi-hop leader selection for effective communication in vehicular ad-hoc networks;and chimp optimization algorithm based feature selection with machine learning for medical data classification.

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DDAL: A Drift Detection Active learning Mechanism 11

DDAL: A Drift Detection Active Learning Mechanism

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11th international conference on Advanced Cloud and Big data, CBD 2023

作者： Li, Shuren Wang, Weinan Jiang, Xiangqian Hua, Jun Mao, Jiawei Lu, Yibin Jia, Xuan Wang, Di Wang, Zhen Lu, Yifei Aerospace System Engineering Shanghai China Nanjing University of Science and Technology China

ISBN: (纸本)9798350345346

Existing active algorithms cannot handle multiple concept drift patterns, discard historical data during retraining, and suffer from imbalanced training data. these limitations result in online algorithms being unable to maintain continuous high-precision detection. If data annotators want to know about the occurrence of concept drift through algorithms, they need to accurately identify concept drift in the data annotation phase. However, in practical scenarios, the data annotation system cannot accurately perceive concept drift. therefore, this paper proposes a method called DDAL (Drift Detection Active learning), which has the ability to perceive concept drift. DDAL manages data queues using time slices and incorporates concept drift detection, bringing forward the concept drift perception of online learning algorithms to the data annotation phase. this makes the model training more accurate and reduces the response time to concept drift. through experiments and theoretical analysis, data annotated using the DDAL algorithm has higher accuracy compared to existing semi-supervised and unsupervised annotation methods. © 2023 IEEE.

关键词： learning algorithms

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Advanced Deep learning Approaches: Utilizing VGG16, VGG19, and ResNet Architectures for Enhanced Grapevine Disease Detection 11

Advanced Deep Learning Approaches: Utilizing VGG16, VGG19, a...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Yadav, Ajay Pratap Singh thapliyal, Nitin Aeri, Manisha Kukreja, Vinay Sharma, Rishabh Iimt College of Engineering Uttar Pradesh Greater Noida India Computer Science and Engineering Graphic Era Hill University Uttrakhand Dehradun248002 India Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

this study aims to give an overall comparison among three of the most powerful convolutional neural network (CNN) architectures-VGG16, VGG19, and ResNet for the classification of leaf diseases, particularly targeting leaf blight, ESCA, black rot, and healthy leaves. With the consequent economic effects of these diseases on viticulture, it is essential to create precise, productive, and scalable diagnostic tools for the pursuit of sustainable agricultural practices. Using a very large dataset of grapevine leaf images acquired from different localities, this study investigates the ability of the models to correctly assign the diseases to the visual symptoms. the models were assessed through a rigorous evaluation process including data preprocessing, augmentation, and stratified k-fold cross-validation approach to determine the overall accuracy, precision, recall, and F1 score. the study results reflect no doubt that the ResNet model surpasses its rivals with an outstanding accuracy of 95% which is followed by VGG19 with 93.5% and VGG16 with 92%. these observations emphasize the power of deep residual learning and the importance of architecture depth in ensuring the accuracy of CNNs in the field of plant disease classification tasks. In addition, the study underlines the need for having holistic and varied datasets that train models to generalize across different environments and disease presentations. the current research adds to the growing knowledge base on the use of deep learning (DL) in agriculture, thus providing insight into the most efficient CNN networks that can be used for grapevine disease recognition. this work contributes to the integration of AI-based diagnostic tools in precision agriculture;this will eventually help improve the current disease management practices and foster sustainable crop production systems. © 2024 IEEE.

关键词： Network architecture

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