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LSTM vs Plot-based CNN for EEG Emotion Detection Tasks

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LSTM vs Plot-based CNN for EEG Emotion Detection Tasks

IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Jared Kelnhofer Marcus Blaisdell Mona Ghandi Wearable Computing REU Program Washington State University Pullman WA USA

ISBN: (纸本)9781665439664

Emotion detection using machine learning and data gathered from an electroencephalogram (EEG) holds the potential for architecture and creating smart adaptive spaces which can respond to the user's current emotional state detected from the Neurophysiological data in real-time. This technology can help people with mental and physical disabilities to have a greater role in shaping their environment and live more independent lives. In this paper, two different machine learning approaches, the Long Short Term memory network, (LSTM) and Convolutional Neural Network (CNN) are compared in order to assess their potential to satisfy this goal of emotion detection. The LSTM network was trained on eight-channel time-series data which had undergone a Fast Fourier Transform, and the CNN was trained on the un-transformed data in the form of a unique plot-image based approach.

关键词： Emotion recognition Electric potential Fast Fourier transforms Machine learning Electroencephalography Real-time systems Convolutional neural networks

Early Detection of Retinitis Pigmentosa

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Early Detection of Retinitis Pigmentosa

IEEE International Conference on Intelligent computing and Information Systems (ICICIS)

作者： Adham Mohammed El Gazoly Karma Yasser Ismail Mahmoud Ahmed El-Tohamy Ali Ahmed Hassan Omar Mohammed Hamed Mohammed Maryam Nabil Al-Berry Hasnaa Sayed Abdo Mohammed Yousra Samir Al Amir Bioinformatics Program Information Systems Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Retinitis pigmentosa (RP) is a debilitating inherited retinal disorder that affects a significant number of individuals worldwide. Existing diagnostic methods have limitations in detecting early-stage RP and they highlight the need for using additional imaging modalities. In this paper, a groundbreaking approach for RP diagnosis is proposed using three imaging modalities and a CNN-based classification model achieving 97.7% accuracy. The proposed model demonstrates superior performance compared to previous work as it achieves higher accuracy. A segmentation model was also proposed to segment pigment spots and provide doctors with effective monitoring of the disease. The proposed segmentation model outperforms previously used methods by achieving 99.61% accuracy and higher values in most of the performance metrics, including specificity, precision, and Area Under Curve (AUC). The Segmentation and classification models were integrated into a web application providing a user-friendly and intuitive tool for RP diagnosis and paving the way for more accessible and effective diagnostic tools. This innovative approach democratizes healthcare by making RP diagnosis affordable and accessible to more people and improving the speed of clinical decision-making.

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An Improved Approach for Semantic Segmentation of Fundus Lesions using R2U-Net

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An Improved Approach for Semantic Segmentation of Fundus Les...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Alejandro Pereira Carlos Santos Marilton Aguiar Daniel Welfer Marcelo Dias Rafaela de Menezes Paulo Roberto Ferreira Fábio Rossi Marcos d’Ornellas Carlos Jesus Haygert Juliano Kazienko Fábio Comim Aurélio Hoppe Juliano Weber Leandro Tavares Leonardo Guths Rodrigo Guerra Postgraduate Program in Computing Federal University of Pelotas - UFPel Pelotas Brazil Federal Institute of Education Science and Technology Farroupilha - IFFar Alegrete Brazil Department of Applied Computing Federal University of Santa Maria - UFSM Santa Maria Brazil Department of Clinical Medicine Federal University of Santa Maria - UFSM Santa Maria Brazil Industrial Technical College Federal University of Santa Maria - UFSM Santa Maria Brazil Department of Clinical Medicine Federal University of Minas Gerais - UFMG Belo Horizonte Brazil Department of Information Systems and Computing Regional University of Blumenau - FURB Blumenau Brazil Federal Institute of Education Science and Technology Farroupilha - IFFar Santo Ângelo Brazil Postgraduate Program in Computing Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil Center of Computational Sciences Federal University of Rio Grande - FURG Rio Grande Brazil

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Diabetic Retinopathy (DR) is a microvascular complication related to diabetes that affects approximately 33% of individuals with this condition and, if not detected and treated early, can lead to irreversible vision loss. Fundus lesions such as Hard and Soft Exudates, Hemorrhages, and Microaneurysms typically identify DR. The development of computational methods to segment these lesions plays a fundamental role in the early diagnosis of the disease. This paper proposes a new approach that uses an R2U-Net combined with data augmentation techniques for segmenting fundus lesions. We trained, adjusted, and evaluated the proposed work in the DDR dataset, achieving an accuracy of 99.87% and a mean Intersection over Union (mIoU) equal 59.69%. Furthermore, we assessed it in the IDRiD dataset, achieving an mIoU of 49.92%. The results obtained in the experiments highlight the potential contribution of the model in the lesion annotations for creating new DR datasets, which is essential given the scarcity of annotations in publicly available datasets.

关键词： Measurement Diabetic retinopathy Accuracy Annotations Semantic segmentation Feature extraction Data augmentation Data models Lesions Hemorrhaging

Goal-oriented inference of environment from redundant observations

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arXiv 2023年

作者： Takahashi, Kazuki Fukai, Tomoki Sakai, Yutaka Takekawa, Takashi Informatics Program Graduate School of Engineering Kogakuin University of Technology and Engineering Japan Neural Coding and Brain Computing Unit Okinawa Institute of Science and Technology Japan Brain Science Institute Tamagawa University Japan

The agent learns to organize decision behavior to achieve a behavioral goal, such as reward maximization, and reinforcement learning is often used for this optimization. Learning an optimal behavioral strategy is difficult under the uncertainty that events necessary for learning are only partially observable, called as Partially Observable Markov Decision Process (POMDP). However, the real-world environment also gives many events irrelevant to reward delivery and an optimal behavioral strategy. The conventional methods in POMDP, which attempt to infer transition rules among the entire observations, including irrelevant states, are ineffective in such an environment. Supposing Redundantly Observable Markov Decision Process (ROMDP), here we propose a method for goal-oriented reinforcement learning to efficiently learn state transition rules among reward-related"core states" from redundant observations. Starting with a small number of initial core states, our model gradually adds new core states to the transition diagram until it achieves an optimal behavioral strategy consistent with the Bellman equation. We demonstrate that the resultant inference model outperforms the conventional method for POMDP. We emphasize that our model only containing the core states has high explainability. Furthermore, the proposed method suits online learning as it suppresses memory consumption and improves learning speed. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

Measuring the contribution of multiple model representations in detecting adversarial instances

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arXiv 2021年

作者： Steinberg, Daniel Munro, Paul Intelligent Systems Program University of Pittsburgh School of Computing and Information University of Pittsburgh

Deep learning models have been used for a wide variety of tasks. They are prevalent in computer vision, natural language processing, speech recognition, and other areas. While these models have worked well under many scenarios, it has been shown that they are vulnerable to adversarial attacks. This has led to a proliferation of research into ways that such attacks could be identified and/or defended against. Our goal is to explore the contribution that can be attributed to using multiple underlying models for the purpose of adversarial instance detection. Our paper describes two approaches that incorporate representations from multiple models for detecting adversarial examples. We devise controlled experiments for measuring the detection impact of incrementally utilizing additional models. For many of the scenarios we consider, the results show that performance increases with the number of underlying models used for extracting representations. Code is available at https://***/dstein64/multi-advdetect. Copyright © 2021, The Authors. All rights reserved.

关键词： Speech recognition

Visualizing representations of adversarially perturbed inputs

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arXiv 2021年

作者： Steinberg, Daniel Munro, Paul Intelligent Systems Program University of Pittsburgh School of Computing and Information University of Pittsburgh

It has been shown that deep learning models are vulnerable to adversarial attacks. We seek to further understand the consequence of such attacks on the intermediate activations of neural networks. We present an evaluation metric, POP-N, which scores the effectiveness of projecting data to N dimensions under the context of visualizing representations of adversarially perturbed inputs. We conduct experiments on CIFAR-10 to compare the POP-2 score of several dimensionality reduction algorithms across various adversarial attacks. Finally, we utilize the 2D data corresponding to high POP-2 scores to generate example visualizations. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learning

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arXiv 2024年

作者： Huang, Chun-Kai Hsieh, Yi-Hsien Chien, Ta-Jung Chien, Li-Cheng Sun, Shao-Hua Su, Tung-Hung Kao, Jia-Horng Lin, Che Taiwan Data Science Degree Program NTU Taiwan Department of Electrical Engineering NTU Taiwan Division of Gastroenterology and Hepatology NTU Hospital Taiwan Hepatitis Research Center NTU Hospital Taiwan Graduate Institute of Clinical Medicine College of Medicine NTU Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taiwan Smart Medicine and Health Informatics Program NTU Taiwan

Multivariate time series (MTS) data, when sampled irregularly and asynchronously, often present extensive missing values. Conventional methodologies for MTS analysis tend to rely on temporal embeddings based on timestamps that necessitate subsequent imputations, yet these imputed values frequently deviate substantially from their actual counterparts, thereby compromising prediction accuracy. Furthermore, these methods typically fail to provide robust initial embeddings for values infrequently observed or even absent within the training set, posing significant challenges to model generalizability. In response to these challenges, we propose SCAlable Numerical Embedding (SCANE), a novel framework that treats each feature value as an independent token, effectively bypassing the need for imputation. SCANE regularizes the traits of distinct feature embeddings and enhances representational learning through a scalable embedding mechanism. Coupling SCANE with the Transformer Encoder architecture, we develop the Scalable nUMerical eMbeddIng Transformer (SUMMIT), which is engineered to deliver precise predictive outputs for MTS characterized by prevalent missing entries. Our experimental validation, conducted across three disparate electronic health record (EHR) datasets marked by elevated missing value frequencies, confirms the superior performance of SUMMIT over contemporary state-of-the-art approaches addressing similar challenges. These results substantiate the efficacy of SCANE and SUMMIT, underscoring their potential applicability across a broad spectrum of MTS data analytical tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

Understanding the Beneficial Role of Transition-Metal Layer Na⁺ Substitution on the Structure and Electrochemical Properties of the P2-Layered Cathode Na2+x Ni2-x/2TeO6

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CHEMISTRY OF MATERIALS 2025年第9期37卷 3040-3053页

作者： Grundish, Nicholas S. Henkelman, Graeme Goodenough, John B. Delmas, Claude Carlier, Dany Seymour, Ieuan D. Univ Texas Austin Mat Sci & Engn Program Austin TX 78712 USA Univ Texas Texas Mat Inst Austin TX 78712 USA Univ Texas Austin Dept Chem Austin TX 78712 USA Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA Univ Bordeaux Bordeaux INP CNRS ICMCBUMR 5026 F-33600 Pessac France CNRS FR 3459 Reseau Francaissur Stockage Electrochim Energie RS2E F-80039 Amiens France Univ Aberdeen Adv Ctr Energy & Sustainabil Sch Nat & Computing Sci Dept Chem Aberdeen AB24 3FX Scotland

Layered Na x MO2 sodium oxide positive electrode materials have experienced renewed interest owing to the current commercial attention on sodium-ion batteries. Although there are many attractive qualities of these materials, they suffer from serious shortcomings owing to Na+ ordering and transition-metal layer gliding that cause a plethora of voltage plateaus during cycling. The P2-layered Na2+x Ni2-x/2TeO6 (0 <= x <= 0.5) system provides a framework for investigating the effect of dual Na+ substitution into the sodium layer and the transition-metal layer of the structure and its effects on the electrochemical properties of the materials. A careful investigation into the synthesis and properties of these materials reveals that the sodium content used during material preparation has a drastic effect on the composition and electrochemical profile of these materials. The sodium substitution disrupts ordering within the transition-metal layer, thereby disrupting Na+ ordering in the adjacent sodium layers. Beyond a critical sodium concentration, the layer stacking shifts, and all voltage plateaus of the P2-Na2Ni2TeO6 material are no longer observed at 4.4 V versus Na+/Na. These results also question the common belief that additional sodium precursor is required when preparing layered sodium oxide cathodes, providing new guidelines for material synthesis and characterization.

关键词： Sodium-ion batteries