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Automated Detection of Capuchinbird Calls in Tropical Forest Recordings Using Deep Learning-Based Spectrogram Analysis

Automated Detection of Capuchinbird Calls in Tropical Forest...

引用

ieee Distributed computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Pallavi Shetty Bhagyashree Chaithra R Devadiga Rajesh B S Disha Shetty Disha Padre Department of MCA Nitte (Deemed to be) University NMAM Institute of Technology Nitte India Department of Computer Science NRAM Polytechnic Nitte India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

This research paper presents an advanced methodology for the automated detection of Capuchinbird (Perissocephalus tricolor) calls in tropical forest recordings, utilizing deep learning-based spectrogram analysis. The primary aim is to enhance biodiversity monitoring by accurately identifying the vocalizations of this elusive species within complex acoustic environments. The proposed system leverages convolutional neural networks (CNNs) to analyze spectrograms derived from audio recordings, effectively distinguishing Capuchinbird calls from background noise and other avian species. The dataset used for training and validation includes a diverse array of recordings collected from various tropical forest locations, ensuring robustness and generalizability of the model. Performance metrics indicate high precision and recall rates, demonstrating the efficacy of the deep learning approach in handling the intricate acoustic patterns of tropical forests. This automated detection framework offers significant improvements over traditional manual and semi-automated methods, providing a scalable and efficient tool for ornithologists and conservationists to monitor Capuchinbird populations and, by extension, the health of tropical ecosystems. The implications of this research extend to broader applications in bioacoustics, contributing to the preservation of biodiversity through advanced technological integration.

关键词： Deep learning Biological system modeling Wildlife Forestry Robustness data models Biodiversity Environmental management Monitoring Spectrogram

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Global-local Fourier Neural Operator for Accelerating Coronal Magnetic Field Model

Global-local Fourier Neural Operator for Accelerating Corona...

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ieee international conference on Big data

作者： Yutao Du Qin Li Raghav Gnanasambandam Mengnan Du Haimin Wang Bo Shen Mechanical and Industrial Engineering NJIT Newark USA Department of Physics NJIT Newark USA Industrial and Manufacturing Engineering FAMU-FSU Tallahassee FL Department of Data Science NJIT Newark USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Exploring the outer atmosphere of the sun has remained a significant bottleneck in astrophysics, given the intricate magnetic formations that significantly influence diverse solar events. Magnetohydrodynamics (MHD) simulations allow us to model the complex interactions between the sun’s plasma, magnetic fields, and the surrounding environment. However, MHD simulation is extremely time-consuming, taking days or weeks for simulation. The goal of this study is to accelerate coronal magnetic field simulation using deep learning, specifically, the Fourier Neural Operator (FNO). FNO has been proven to be an ideal tool for scientific computing and discovery in the literature. In this paper, we proposed a global-local Fourier Neural Operator (GL-FNO) that contains two branches of FNOs: the global FNO branch takes downsampled input to reconstruct global features while the local FNO branch takes original resolution input to capture fine details. The performance of the GL-FNO is compared with state-of-the-art deep learning methods, including FNO, U-NO, U-FNO, Vision Transformer, CNN-RNN, and CNN-LSTM, to demonstrate its accuracy, computational efficiency, and scalability. Furthermore, physics-based analysis from domain experts is also performed to demonstrate the reliability of GL-FNO. The results show that GL-FNO not only accelerates the MHD simulation (a few seconds for prediction, more than ×20,000 speed up) but also provides reliable prediction capabilities, thus greatly contributing to the understanding of space weather dynamics. Our code implementation is available at https://***/Yutao-0718/GL-FNO.

关键词： Deep learning Solid modeling Magnetohydrodynamics Accuracy Atmospheric modeling Computational modeling Magnetic domains Predictive models Magnetic fields Reliability

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Tomo-NeRL: tomographic neural representation learning for implicit CT image reconstruction

Tomo-NeRL: tomographic neural representation learning for im...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoqin Tang Boheng Tan Yuanhao Guo Xianping Yu Jake Kendrick Mark Reynolds College of Computer and Information Science Southwest University Chongqing China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Research Institute of Highway Ministry of Transport Beijing China School of Physics Mathematics and Computing The University of Western Australia Perth Australia Centre for Advanced Technologies in Cancer Research (CATCR) Perth Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

CT image reconstruction is a challenging inverse problem of estimating image intensities from sensor signals. While deep learning-based methods hold promise, they are typically classified into fully-supervised, self-supervised, and case-by-case approaches, each offering distinct advantages and limitations. Among these, case-by-case reconstruction techniques, exemplified by models like NeRP, provide the advantage of flexible data-sampling and image sizes, making them well-suited for the complex CT inverse problem. However, existing case-by-case models encounter difficulties in achieving precise reconstructions, particularly in sparse-view scenarios. To address this limitation, we propose tomo-NeRL, a novel implicit reconstruction model that incorporates tomographic information of individual pixels within the reconstruction network. By harnessing this tomo-graphic data for implicit inverse learning, tomo-NeRL enhances reconstruction quality while effectively mitigating artifacts. The core innovation of tomo-NeRL lies in defining and extracting tomographic insights, such as visual characteristics and spatial variations specific to tomographic imaging, from the sinogram data for individual pixels. Moreover, our work introduces an adaptive inversion framework that amalgamates tomographic information from diverse projection angles, constructing a latent feature space that enhances the reconstruction process. Extensive experiments unequivocally showcase tomo-NeRL’s outstanding reconstruction performance, excelling in artifact suppression and detail preservation, especially in sparse-view scenarios.

关键词： Representation learning Visualization Technological innovation Inverse problems Computed tomography Training data Tomography Reconstruction algorithms data mining Image reconstruction

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Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics

Boundary-Guided Learning for Gene Expression Prediction in S...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingcheng Qu Yuncong Wu Donglin Di Anyang Su Tonghua Su Yang Song Lei Fan Faculty of Computing Harbin Institute of Technology Harbin China School of Astronautics Harbin Institute of Technology Harbin China Space AI Li Auto Beijing China College of Software Jilin University Changchun China Computer Science and Engineering UNSW Sydney Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Spatial transcriptomics (ST) has emerged as an advanced technology that provides spatial context to gene expression. Recently, deep learning-based methods have shown the capability to predict gene expression from WSI data using ST data. Existing approaches typically extract features from images and the neighboring regions using pretrained models, and then develop methods to fuse this information to generate the final output. However, these methods often fail to account for the cellular structure similarity, cellular density and the interactions within the *** this paper, we propose a framework named BG-TRIPLEX, which leverages boundary information extracted from pathological images as guiding features to enhance gene expression prediction from WSIs. Specifically, our model consists of three branches: the spot, in-context and global branches. In the spot and in-context branches, boundary information, including edge and nuclei characteristics, is extracted using pretrained models. These boundary features guide the learning of cellular morphology and the characteristics of microenvironment through Multi-Head Cross-Attention. Finally, these features are integrated with global features to predict the final *** experiments were conducted on three public ST datasets. The results demonstrate that our BG-TRIPLEX consistently outperforms existing methods in terms of Pearson Correlation Coefficient (PCC). This method highlights the crucial role of boundary features in understanding the complex interactions between WSI and gene expression, offering a promising direction for future research. Codes are available at: https://***/WcloudC0416/BG-TRIPLEX

关键词： Learning systems Pathology Technological innovation Image edge detection Transcriptomics Morphology Predictive models Feature extraction Gene expression data mining

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End-to-End On-Orbit Objects Detection with ConvNets

End-to-End On-Orbit Objects Detection with ConvNets

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ieee international conference on Systems, Man and Cybernetics

作者： Bo Ru Pengrong Hou Xiang Li Qinghao Chu Zikang Zeng Chenming Zhang Zhelong Wang School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

As space activities expand, the quantity of space debris also increases, posing significant risks to spacecraft and infrastructure. Space situational awareness (SSA) is essential for avoiding collisions and limiting the generation of extra debris. Accurate and efficient detection of space objects plays a critical role in achieving this goal. Our research focuses on the development of detection algorithms that are both precise and quick, taking into account the real-time and safety of spacecraft operations in orbit. For the first time, we take a fully Convolutional Neural Networks (ConvNets) to run the query-based end-to-end object detection for SSA. We further compare its performance with the newest YOLOv9 algorithm. This is an innovative attempt at SSA. First of all, it does not require predefined a priori anchor boxes or complex post-processing strategies such as Non-Maximum Suppression (NMS), and can directly achieve end-to-end target detection. Secondly, the fully ConvNets are selected as the basic framework, which not only retains the advantages of self-attention mechanism, but also greatly improves the computing efficiency. These methods show outstanding performance on the challenging SPARK data set. The fully ConvNets approach achieves end-to-end detection by utilizing the query attention mechanism, excluding the need for complicated post-processing in traditional object detection methods and with higher efficiency. YOLOv9 involves an enhanced feature pyramid fusion and a more powerful detection head, potentially resulting in higher precision. Following that, we will thoroughly assess the speed, accuracy, and trade-offs of the two algorithms using actual data sets in order to deliver an efficient and dependable solution for detecting targets in aerospace sensing missions.

关键词： Space vehicles Accuracy Attention mechanisms Computational modeling Space debris Object detection Feature extraction data models Computational efficiency Sparks

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Detection of Barrett's Esophagus in Endoscopic Images Using Machine Learning Algorithms

Detection of Barrett's Esophagus in Endoscopic Images Using ...

引用

Communication, computing and Signal Processing (IICCCS), ieee international conference on

作者： Ramy Riad Al-Fatlawy Divya Saurabh Khare Nitika A N Satyanarayana Rajashree Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Information Technology IES University Bhopal M.P. India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India ECE Department Sreenidhi Institute of Science and Technology Hyderabad India Department of CSE Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

BE is a precursor to esophageal adenocarcinoma in which the patient is at a higher risk compared to those who the disease is not diagnosed. As for cancer, its early detection and proper handling should be of a significant concern to avoid chances of becoming CANCER. This paper aims at investigating the application and development of machine learning particularly Convolutional Neural Network (CNN) for automated identification of BE when seen in Endoscopy. Utilizing a mixture of open-source databases and specific clinical materials, a complex and well-established CNN model was created and calibrated to diagnose BE. Its efficacy was further supported by high performance rates such as the accuracy of 91% and sensitivity and specificity of 89% and 92% respectively along with AUC ROC value of 0. 94. To solve for the overfitting problem, data augmentation and transfer learning strategies were applied to improve the generalization of the model. Also, to increase the explainability of the deep-learning-based prediction model, certain explainable AI techniques were adopted. The model has been compared with the traditional ML model as well as the basic CNN, and the improved model seems to perform better. About the early detection of BE, this study reveals that the application of the ML-based tools shall provide new ideas and methods to help clinicians to detect BE at its early stage so as to improve the quality of patients’ lives and the survival rate of death from esophageal adenocarcinoma. The clinical photographs generated by this software still require further fine-tuning and refinement to alleviate issues that hinder its implementation in clinical use and to integrate this software seamlessly in clinical settings.

关键词： Accuracy Explainable AI Transfer learning data augmentation data models Software Convolutional neural networks Usability Esophagus Cancer

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Authenticated Subgraph Matching in Hybrid-Storage Blockchains

Authenticated Subgraph Matching in Hybrid-Storage Blockchain...

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international conference on data Engineering

作者： Siyu Li Zhiwei Zhang Meihui Zhang Ye Yuan Guoren Wang Beijing Institute of Technology Beijing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graphs serve as an essential data structure to model complex relationships in a variety of applications, such as social networks, web graphs, and chemical informatics. Due to the high cost of maintaining large-scale graph data and executing graph queries, data owners often outsource their graph data to a third-party service provider for graph processing. In this scenario, it is crucial to ensure the integrity of query results, as the provider may have the incentive to return only partial or tampered results to save computing resources or serve their own interests. Blockchain, as a promising solution for secure data storage and retrieval, opens up new opportunities for data management in such scenarios. To scale the blockchain, many works have been conducted using off-chain storage while ensuring the integrity of query results for key-value data in hybrid-storage blockchain architectures. To our knowledge, there is no work to enable the blockchain to support subgraph matching queries. In this paper, we present a novel approach to support authenticated subgraph matching queries for large graphs kept off-chain. We first design the authenticated data structure as MELTree and keep the digests of the roots on-chain. We propose the verification object (VO) construction algorithm AMatching for queries to ensure the completeness and soundness of the results. To further reduce the cost, we propose AMatching* based on a bidirectional search including forward search and reverse search. Moreover, we further optimize the on-chain storage cost by proposing MVPTree, which organizes the structures for vertices and only needs to keep one root digest on-chain for verification. Experimental results show that the proposed algorithms and the optimizations improve the performance significantly.

关键词： Costs Social networking (online) Memory data structures data engineering data models Blockchains

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Mixup data Augmentation and Channel-Spatial Attention Mechanisms for Facial Emotion Recognition

Mixup Data Augmentation and Channel-Spatial Attention Mechan...

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ieee international Symposium on Information (IT) in Medicine and Education, ITME

作者： Dewei Yu Chuijian Kong Xianpiao Tang Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal Universitya Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the rapid advancement of computer vision, facial expression recognition has made significant progress in areas like human-computer interaction, mental health, driver assistance, and education. However, traditional facial expression recognition techniques exhibit poor robustness under complex backgrounds and varying lighting conditions, making them susceptible to environmental noise and challenging to accurately capture and classify complex emotional states. Additionally, these techniques often lack in data augmentation and model generalization capabilities, leading to overfitting on training samples and insufficient recognition of unseen samples. To address these limitations, this paper proposes a novel convolutional neural network model—HSCNet (A Hybrid data Augmentation and Channel-Spatial Attention Mechanisms Neural Network). The HSCNet model first employs the Mixup data augmentation technique to generate new training samples, then utilizes the Res2Net50 backbone network combined with the CBAM attention mechanism and average-max pooling techniques to extract significant features while suppressing redundant ones. This approach not only enhances feature extraction accuracy but also significantly improves the model’s generalization ability and robustness in facial expression recognition tasks. Finally, tested on the public dataset FERPlus, which includes eight types of emotions, the HSCNet model achieved an accuracy rate of 89.72%, demonstrating excellent performance and robustness.

关键词： Training Emotion recognition Attention mechanisms Accuracy Face recognition Lighting Feature extraction data augmentation data models Robustness

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Effective Entry-Wise Flow for Molecule Generation

Effective Entry-Wise Flow for Molecule Generation

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international conference on data Engineering

作者： Qifan Zhang Junjie Yao Yuquan Yang Yizhou Shi Wei Gao Xiaoling Wang School of Computer Science and Technology East China Normal University Software Engineering Institute East China Normal University Shanghai Key Laboratory of Trustworthy Computing Department of Chemistry University of Massachusetts Boston University of Michigan Ann Arbor.

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These models are particularly suited for handling the symmetrical and complex chemical structures often encountered in molecular datasets. Despite their promising nature, normalizing flow-based models for molecule generation face considerable challenges. The complexity of molecule representation, the rigorous demands of optimization, and the scarcity of training labels in molecular datasets contribute to these difficulties. Additionally, adequately and comprehensively learning the distribution of molecular datasets remains a formidable task. In this paper, we delve into the intricate entry-wise modules in vanilla flows, introducing an effective variation of flow-based models. Our proposed approach innovatively encapsulates affine coupling transformations within normalizing flows. Furthermore, we deconstruct existing invertible flow models, integrating them with newly developed entry-wise transformations. Our experimental studies demonstrate that these proposed entry-wise modules, when incorporated into standard flow-based models, surpass other generative models in performance on various representative datasets and generation tasks. Notably, in the context of low-resourced molecular graph generation, our model achieves remarkable performance compared to its counterparts.

关键词： Training Couplings Materials science and technology Interpolation data engineering Drug discovery Task analysis

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Remote Motor Rehabilitation: EMG-IMU based Deep Learning Model Improves the Estimate of Wrist Kinematics

Remote Motor Rehabilitation: EMG-IMU based Deep Learning Mod...

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Annual international conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Ilaria Siviero Florian Helmhold Andreas M. Ray Carlos Bibián Gloria Menegaz Ander Ramos Murguialday Silvia F. Storti Dept. of Computer Science University of Verona Verona Italy Inst. of Medical Psychology and Behavioral Neurobiology University of Tübingen Tübingen Germany GTC of Neuroscience / IMPRS for Cognitive and Systems Neuroscience Tübingen Germany Dept. of Engineering for Innovation Medicine University of Verona Verona Italy TECNALIA Basque Research and Technology Alliance San Sebastian Spain Dept. of Neurology and Stroke University of Tübingen Tübingen Germany Athenea Neuroclinics San Sebastian Spain

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Technology for motor rehabilitation faces challenges in uncontrolled settings, such as at home. In these real-world scenarios, robust signals like electromyographic (EMG) and inertial measurement unit (IMU) data are crucial for decoding continuous human actions. Classical modeling methods, such as linear, adaptive, or static filters, lack the capacity to capture complex relationships between surface EMG and kinematics, as well as generalizability across subjects. We propose a deep learning (DL) model for the continuous decoding of hand motion. Custom-made wearable devices acquired EMG-IMU data from 27 healthy subjects performing a wrist flexion/extension task. Two regression models were compared with a hybrid convolutional neural network and a gated recurrent unit. To address inter-subject variability, a leave-one-subject-out cross-validation approach was implemented. The DL model showed a mean R 2 increase of 0.18 compared to the polynomial regressor. Our approach enhances wrist kinematics decoding providing a generalized model based on data captured with wearable devices. The findings hold potential for innovative home-based telemedicine solutions in motor rehabilitation.

关键词： Wrist Deep learning Adaptation models Biological system modeling Telemedicine Kinematics Motors Electromyography Decoding Wearable devices

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