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Subject Generalization in Classifying Imagined and Spoken Speech with MEG 11

Subject Generalization in Classifying Imagined and Spoken Sp...

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

作者： Dash, Debadatta Ferrari, Paul Babajani-Feremi, Abbas Harwath, David Borna, Amir Wang, Jun Univ Texas Austin Dell Med Sch Dept Neurol Austin TX 78712 USA Helen DeVos Childrens Hosp Spectrum Hlth Grand Rapids MI USA Univ Florida Dept Neurol Gainesville FL USA Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Sandia Natl Labs AO Sensing & EC Engn POB 5800 Albuquerque NM 87185 USA Univ Texas Austin Dept Speech Language & Hearing Sci Austin TX 78712 USA Univ Texas Austin Dept Neurol Austin TX 78712 USA

ISBN: (纸本)9781665462921

Speech decoding-based brain-computer interfaces (Speech-BCIs) decode speech directly from brain signals, which have the potential to offer faster and natural communication to patients with locked-in syndrome than the current BCI-spellers. On account of the huge cognitive variance among subjects, most of the current speech-BCI models have focused on subjectdependent decoding where the training and evaluation of the decoding algorithms use data from the same participants. these models do not generalize across individuals and, thus, are limited by the small data size that can be obtained from a single participant. Few studies have attempted subject-independent decoding but the performances are sub-par at best and significantly lower than subject-dependent models. To address this issue, we evaluated imagined and overt speech decoding with magnetoencephalography (MEG) recordings of eight speakers in a generalizable subject-independent setting. We used recent domain adaptation techniques including feature augmentation and curriculum learning to introduce generalizability to the decoding model. Our results indicated that domain adaptation techniques can be efficient in subject-independent decoding. the best performance was obtained with a curriculum learning based adaptation technique that resulted in decoding accuracy was close to that in subject-dependent decoding. Our findings show the possibility of subject generalization in neural speech decoding.

关键词： curriculum learning MEG speech-BCI

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An Offline Reinforcement learning Approach for Smart Quantitative Trading through Pre-imitation 11

An Offline Reinforcement Learning Approach for Smart Quantit...

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11th international Symposium on Project Management, ISPM 2023

作者： Shi, Shengyun Li, Shi-Ting Li, Guoxi School of Accounting Zhanjiang University of Science and Technology Guangdong Province Zhanjiang524000 China Business School The University of Melbourne Grattan Street ParkvilleVIC3010 Australia

ISBN: (纸本)9781713877134

Quantitative trading (QT) is an important field of intelligent decision-making. In recent years, the rapid development of reinforcement learning algorithm has driven the research in this field. the off-line reinforcement learning algorithm solves the cold start problem in the practical application of reinforcement learning algorithm. It can be predicted that the offline reinforcement learning algorithm will have a good application prospect in the field of quantitative trading. In this paper, a workflow using off-line reinforcement learning method to deal with quantitative trading problem is proposed, which improves the adaptability of the algorithm to this field. the method is verified on FinRL containing Nasdaq-100 data. © (2023) by Aussino Academic Publishing House All rights reserved.

关键词： Reinforcement learning

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Enhancing E-commerce Fashion Sales through Personalized Recommendation Systems 18

Enhancing E-commerce Fashion Sales through Personalized Reco...

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

作者： Subaranjani, T. Kannaiyan, Sathiyapriya Parvathy, S. Shwetha, P.R. The Oxford College of Engineering Dept. of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9789380544519

Personalized recommendation systems are pivotal in the thriving e-commerce fashion sector, enriching customer experiences and driving sales. this paper introduces an innovative method to enhance e-commerce fashion sales through personalized recommendations. the model merges Singular Value Decomposition (SVD) Reranking with customer grouping, yielding tailored product suggestions for distinct customer segments. the study comprehensively explores multiple recommendation techniques, assessing their performance using Mean Average Precision (MAP) scores. Results demonstrate the superiority of the proposed model, achieving a validation score of 0.032007. this success is attributed to the model's ability to capture latent features and offer personalized suggestions based on customer group traits. this research underscores the significance of personalized recommendations in boosting ecommerce fashion sales. the proposed approach, SVD Reranking with customer grouping, excels in providing personalized suggestions across diverse customer segments. these findings contribute to the advancement of recommendation systems in fashion, fostering customer satisfaction, minimizing returns, and bolstering sustainability. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Customer Experience data Analytics Ecommerce Fashion Sales Personalized Recommendation Systems Privacy Concerns Singular Value Decomposition (SVD) Reranking Time-based Popularity Scores User Engagement

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Automatic recognition and license plate detection model based on OpenCV and Machine learning 11

Automatic recognition and license plate detection model base...

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11th international conference on Software Process Improvement (CIMPS) - Applications in Software engineering

作者： Ccoto Huallpa, Elias Sullon Macalupu, Angel Abel Otazu Luque, Jorge Eddy Sanchez-Garces, Jorge Univ Peruana Union Escuela Profes Ingn Sistemas Carretera Salida ArrequipaKm 6 Puno Peru

ISBN: (纸本)9798350398960

Automatic number plate recognition (ALPR) is an important task with many applications in intelligent transportation and surveillance systems. this research takes into consideration the functions of image processing for the detection and recognition of number plates;which can come from noisy sources, low illumination, different angles and distances taken from the images (uncontrolled environments);most of the existing automated number plate recognition systems only work in a controlled environment where images are captured from a right angle with good illumination and clarity [ 25]. According to [11] in uncontrolled environments the probability of recognising the characters on the plates decreases and this has been observed in the research. To achieve image processing, morphological transformation, Gaussian smoothing and Gaussian thresholding were used, then 3 different algorithms K-NN, SVM and Tesseract were used for character recognition, each algorithm with their respective hyperparameters for optimisation. the images were separated into two groups, the first with 80 images taken from different angles and distance (uncontrolled environment) where the best Overall accuracy was obtained with 86 % and the second group were images taken at a right angle and similar distance (controlled environment), this group obtained an Overall accuracy of 95.5 %.

关键词： KNN SVM Tesseract OpenCV Machine learning y hiperparametros

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Improved Ingredients-based Recipe Recommendation Software using Machine learning 11

Improved Ingredients-based Recipe Recommendation Software us...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Ashraf, Mariam Tarek, Khadija Fathy, Naglaa Mahmoud, Mariam Emad, Rana Khaled, Khadija Amr, Noran Ain Shams University Faculty of Computer and Information Science Software Engineering Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. the system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. the recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. this feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences. © 2023 IEEE.

关键词： Image recognition

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Do Single Beat ECGs have Same Credibility as Continuous ECG Signal in Multi-Class Classification using Machine learning Models 11

Do Single Beat ECGs have Same Credibility as Continuous ECG ...

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11th IEEE international conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Bhardwaj, Arya Kumar, R. Pradeep Sivaraman, J. Pal, Kunal Neelapu, Bala Chakravarthy National Institute of Technology Department of Biotechnology and Medical Engineering Rourkela Odisha India Institute of Cardiac Care MIOT International Chennai600089 India

ISBN: (纸本)9798350308433

Artificial intelligence has proven to be a boon for humanity in various field of science, however meaningful results using Machine learning (ML) and Deep learning (DL) models depends on various factors. One of the factors is to feed the model with large size datasets. For this purpose, segmentation of ECG into Single Beat ECGs (SB-ECG) has become prominent. this study attempts to investigate whether there is an effect of using SB-ECG compared to the continuous recorded ECGs on the model output. the aim of this work is to examine and compare the effect of single beat and continuous ECGs on the ML model multi-class classification performance. 120 ECGs of Sinus Rhythm (SR), Sinus Tachycardia (ST) and Atrial Tachycardia (AT) recorded for 10 seconds using standard 12 lead were retrieved from online database. Two datasets were prepared of 120 and 1687 samples of continuous ECG and SBECG, respectively. the datasets were fed to the ML model and their performance were evaluated. Extra trees model gave the best performance for both datasets. Continuous ECG recording gave the 10-fold cross validated data accuracy of 77.36% and SB-ECGs gave 92.97% with Area Under Curve (AUC) score of 88.07 and 98.92 respectively. SB-ECG gave better classification performance than the continuous ECG recording. SB-ECG can be used for enhanced clinical diagnosis with better accuracy and reduced false outcomes. Also, the study on SB-ECG may prove to be highly efficient in paroxysmal atrial fibrillation detection where episodes of arrhythmia are sudden. © 2024 IEEE.

关键词： Electrocardiograms

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Exploration on Blended Teaching Mode of Python Programming Based on PBL 11

Exploration on Blended Teaching Mode of Python Programming B...

引用

11th international conference on Information and Education Technology, ICIET 2023

作者： Zhu, Yingxue Liu, Mingyou Wang, Yue Wu, Tao Guizhou Medical University School of Biology and Engineering Guiyang Guizhou China

ISBN: (纸本)9781665465489

Python programming is widely used in scientific computing, data processing, visualization, image processing, natural language processing, machine learning, artificial intelligence and other fields, and more and more majors offer this course to provide strong support for cultivating high-quality composite "new engineering"talents. this paper explores the teaching of Python programming course for biomedical engineering majors in view of the shortcomings of the current teaching of Python programming, from several aspects such as the objectives and ideas of hybrid teaching, the content and structure of hybrid teaching in PBL mode, the design of online and offline teaching steps and teaching activities, and the design of course assessment system. the aim is to enhance students' self-learning ability and motivate them to learn, so as to cultivate their comprehensive and innovative practical application ability. © 2023 IEEE.

关键词： Python

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NEW INSIGHTS FOR thE STABILITY-PLASTICITY DILEMMA IN ONLINE CONTINUAL learning 11

NEW INSIGHTS FOR THE STABILITY-PLASTICITY DILEMMA IN ONLINE ...

引用

11th international conference on learning Representations, ICLR 2023

作者： Jung, Dahuin Lee, Dongjin Hong, Sunwon Jang, Hyemi Bae, Ho Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of DeepBrain AI Inc. Korea Republic of Department of Cyber Security Ewha Womans University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

the aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenario of online continual learning, wherein data comes strictly in a streaming manner, the plasticity of online continual learning is more vulnerable than offline continual learning because the training signal that can be obtained from a single data point is limited. To overcome the stability-plasticity dilemma in online continual learning, we propose an online continual learning framework named multi-scale feature adaptation network (MuFAN) that utilizes a richer context encoding extracted from different levels of a pre-trained network. Additionally, we introduce a novel structure-wise distillation loss and replace the commonly used batch normalization layer with a newly proposed stability-plasticity normalization module to train MuFAN that simultaneously maintains high plasticity and stability. MuFAN outperforms other state-of-the-art continual learning methods on the SVHN, CIFAR100, miniImageNet, and CORe50 datasets. Extensive experiments and ablation studies validate the significance and scalability of each proposed component: 1) multi-scale feature maps from a pre-trained encoder, 2) the structure-wise distillation loss, and 3) the stability-plasticity normalization module in MuFAN. Code is publicly available at https://***/whitesnowdrop/MuFAN. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Stability

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CoLabel: a collaborative labeling platform for remote sensing imagery 11

CoLabel: a collaborative labeling platform for remote sensin...

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Jiang, Liangcun Li, Feng Liu, Jie Yue, Peng Wuhan University of Technology School of Resources and Environmental Engineering Wuhan China Wuhan University School of Remote Sensing and Information Engineering Wuhan China

ISBN: (纸本)9798350303513

With the rapid advancement of artificial intelligence in the interpretation of remote sensing imagery, there is a growing demand for a large volume of remote sensing training data in both academic and industrial sectors. However, the traditional approach to data annotation takes place in a standalone environment, resulting in low work efficiency. this creates an immediate need to develop a web platform that facilitates collaborative annotations, allowing for the fully utilization of collective intelligence from experts in the field. this paper introduces CoLabel, a user-friendly, web-based platform designed to streamline the process of labeling remote sensing images. CoLabel enables multiple users to collaboratively label remote sensing images through its intuitive interface. It supports various labeling tasks, including object detection and semantic segmentation. To ensure the quality of training data before publication, the platform incorporates a quality review mechanism that conducts thorough inspections of annotated data. By providing robust training data support, CoLabel contributes to the research and applications in the field of intelligent interpretation of remote sensing. © 2023 IEEE.

关键词： Remote sensing

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LLM-Project: automated engineering Task Planning via Generative AI and WBS Integration 14

LLM-Project: Automated Engineering Task Planning via Generat...

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14th IEEE international conference on Cyber Technology in Automation, Control, and intelligent Systems, CYBER 2024

作者： Zhen, Yue Bi, Sheng Tang, Shuo Lu, Xing-Tong Pan, Wei-Qin Shi, Hai-Peng Chen, Zi-Rui Fang, Yi-Shu Wang, Xin-Meng South China University of Technology School of Computer Science and Engineering Guang Zhou510006 China University of Washington Human Centered Design and Engineering Seattle98195 United States Northeastern University Khoury College of Computer Sciences Boston02115 United States

ISBN: (纸本)9798331506056

We propose and implement a task planning system that integrates a series of validated robotic instructions, deep learning models, and demonstration cases, into a larger structure using the Work Breakdown Structure (WBS) which is commonly used in engineering project management. By organizing these small-scale solutions in a sequence based on temporal and resource dependencies, we use simulations and evaluation functions to select the optimal structure as the Standard Operating Procedure (SOP). these SOPs are then used to train a generative AI, enabling it to mimic human experts in generating high-level structures according to real-world needs and perform minor parameter generalizations to address issues involving temporal dependencies, spatial relationships, and resource allocation. the generated high-level structures are gradually transformed into low-level operations to execute actual tasks. the system records the execution results for automatic or manual analysis, and the feedback is used to continuously improve the generative AI's output. Our work lays the foundation for further research on the innovative application of combining generative AI with WBS data in robotic task planning. https://***/NOMIzy/LLM-Project. © 2024 IEEE.

关键词： Resource allocation

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