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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： P Varun T. Sunitha Mythili Nagalingam P. Nithiya S Selvakumaran T A Mohanaprakash Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Department of Computer Science and Engineering St. Joseph's Institute of Technology OMR Chennai Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Department of Computer Science and Engineering P.B college of engineering Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Surapet Chennai Department of Computer Science and Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities.

关键词： Training Photovoltaic systems Cloud computing Predictive models Inverters Data models Smart grids

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Salient Part-Aligned and Keypoint Disentangling Transformer for Person Re-Identification in Aerial Imagery

Salient Part-Aligned and Keypoint Disentangling Transformer ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Junyang Qiu Zhanxiang Feng Lei Wang Jianhuang Lai School of Computer Science and Engineering Sun Yat-sen University China School of Systems Science and Engineering Sun Yat-sen University China Pazhou Lab (HuangPu) Guangzhou China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Person re-identification (Re-ID) in aerial imagery aims to retrieve individuals utilizing the UAV surveillance platform. However, the unpredictable changing views of UAVs result in failing to attend consistent foreground regions and achieve semantic alignment. Existing works are limited by coarse-grained part-aligned division and disturbance-sensitive self-attention mechanism. To address these issues, we propose a transformer-based Salient Part-Aligned and Keypoint Disentangling (SPAKD) framework to focus on salient human body regions and align semantic parts with the assistance of keypoints, which consists of a Salient Part-Aware Cross-Attention (SPACA) module and a Keypoint-Assisted Decoder (KAD) module. Specifically, SPACA enhances relationships between salient foreground regions and the whole image, thereby obtaining discriminative part features. KAD employs learnable part prototypes to disentangle human keypoints and aligns decoupled keypoint-assisted features with salient foreground parts based on their affinity information. Extensive experiments demonstrate that our method achieves state-of-the-art performance on aerial-based person Re-ID datasets.

关键词： Body regions Visualization Surveillance Semantics Prototypes Transformers Autonomous aerial vehicles

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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Fashion Recommendation System for E-Commerce using Deep Learning Algorithms

Fashion Recommendation System for E-Commerce using Deep Lear...

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International Conference on Computing and Networking Technology (ICCNT)

作者： M S Arunkumar R Gopinath M Chandru R Suguna S Deepa V Omprasath Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu Department of Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchgode Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the increasingly competitive outlook of ecommerce, personalized recommendation systems have emerged as pivotal tools to enhance user experience and drive sales. This paper proposes a novel Fashion Recommendation System (FRS) leveraging deep learning techniques to offer personalized fashion suggestions to users. The system utilizes a combination of convolutional neural networks (CNNs) for image feature extraction. Through a comprehensive analysis of user interactions and preferences, the FRS generates tailored recommendations that align with individual style preferences and current trends. The work proposes a novel approach that utilizes the ResNet50 algorithm to analyze images of fashion items, extracting meaningful features that capture the essence of style, color, and texture. By training the ResNet50 model on vast datasets of fashion images, thereby enhancing its ability to make accurate recommendations. Through the integration of user preferences and historical data, this system can generate personalized fashion recommendations tailored to each user’s unique style and preferences, thus enhancing user satisfaction and engagement. By harnessing the power of deep learning and the ResNet50 algorithm, fashion retailers can offer personalized shopping experiences, increase customer retention, and drive sales.

关键词： Deep learning Training Image color analysis Feature extraction Market research User experience Electronic commerce Convolutional neural networks Recommender systems Residual neural networks

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SPRGAN: Streamlined Progressive Refinement for Adversarial Point Cloud Video Upsampling

SPRGAN: Streamlined Progressive Refinement for Adversarial P...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Zhiyong Liu, Ruyu Wang, Chaochao Zhang, Xianchao Zhang, Jianhua Liu, Xiufeng School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Information Science and Technology Hangzhou Normal University Hangzhou China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Zhejiang China Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark

ISBN: (纸本)9798350368741

Getting dense, uniform, time-series point cloud data is critical for effective rendering. However, due to the limited computational power of edge devices, existing methods cannot achieve real-time results, which affects the visual quality of the consumer experience. To effectively address this issue, this paper presents a self-supervised adversarial upsampling method for point cloud video streams called SPR-GAN. In the generator, we design the Temporal Iterative Graph module to learn local features for each frame and captures long-range spatial information using three iterations of graph convolution operations. Then the Contextual Temporal Fusion module is developed to merge information between different frames, synthesizing temporal information and enriching the dynamic feature representation of the point cloud. Meanwhile, in the discriminator, we introduce the Efficient Shape module. Through dynamic graph convolution operations and stacked learning, it significantly improves the resolution efficiency of global shape information in point clouds. The final experiments show that the proposed method exhibits high practicality and superiority. The model achieves a good result on both the D-FAUST and DeformingThing4D-Animals datasets. © 2025 IEEE.

关键词： point cloud sequence Point cloud upsampling self-supervised temporal aggregation

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Noise-Robust Sleep States Classification Model Using Sound Feature Extraction and Conversion

Noise-Robust Sleep States Classification Model Using Sound F...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sangkeun Ko Seongho Min Ye Shin Choi Woo-Je Kim Suan Lee School of Computer Science Semyung University Jecheon South Korea Industrial and Information Systems Engineering Seoul National University of Science and Technology Seoul South Korea

This study proposes an effective state classification model for sleep, which is crucial for improving daily functioning and overall quality of life. We delve into the extraction of auditory features from sleep-related sounds, such as snoring and teeth grinding, and apply five distinct image transformations-Recurrence Plots (RP), Markov Transition Fields (MTF), Gramian Angular Summation Fields (GASF), Gramian Angular Difference Fields (GAD F), and Short-Time Fourier Transform (STFT)-to accurately delineate sleep states. Our research introduces an innovative deep learning model adept at classifying these states based on the images obtained from these transformations. Furthermore, we rigorously test the model's resilience to noise by introducing varying levels (0%, 25%, 50%, and 75%) and observe that the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, particularly when combined with the STFT technique, consistently outperforms under all noise conditions, achieving accuracies between 99.55% and 98.98%. The findings of this research significantly contribute to the fields of sleep analysis and the study of sleep disorders, offering a robust framework for understanding and classifying sleep states.

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Advances in a DSL to Specify Smart Contracts for Application Integration Processes 25

Advances in a DSL to Specify Smart Contracts for Application...

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25th Ibero-American Conference on Software engineering, CIbSE 2022

作者： Dornelles, Eldair F. Parahyba, Fernando Frantz, Rafael Z. Roos-Frantz, Fabricia Reina Quintero, Antonia M. Molina-Jiménez, Carlos Bocanegra, José Sawicki, Sandro Unijuí University Brazil Department of Language and Systems University of Seville Spain Department of Computer Science and Technology University of Cambridge United Kingdom Department of Systems and Computing Engineering Universidad de los Andes Colombia

ISBN: (纸本)9781713855583

Several languages have been developed for writing smart contracts for specific domains, such as health, finance, and business processes. However, none of them includes the constructors needed for writing smart contracts used in application integration processes which have their own particularities. Such smart contracts are responsible for monitoring the communication between the process and the applications to guarantee that the rights of the parties involved in the integration process are observed. To cover the gap, this paper discusses a domain-specific language for writing smart contracts in the application integration domain. It provides constructors to write smart contracts with a level of abstraction close to the natural language used in conventional contracts. © 2022 CIbSE 2022 - XXV Ibero-American Conference on Software engineering. All rights reserved.

关键词： Smart contract

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A Framework Using Federated Learning for IoT-Based Forest Fire Prediction

A Framework Using Federated Learning for IoT-Based Forest Fi...

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IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

作者： Richard Purcell Kshirasagar Naik Chung-Horng Lung Marzia Zaman Srinivas Sampalli Abdul Mutakabbir Faculty of Computer Science Dalhousie University Halifax NS Canada Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Systems and Computer Engineering Carleton University Ottawa ON Canada Research and Development Cistel Technology Ottawa ON Canada

Forest fires are a growing threat to human commu-nities. The Canadian Wildland Fire Information System gives realtime information to fire management agencies and the public. However, machine learning use for forest fire ignition classification prediction within the platform and ones like it, is yet to be fully realized. We propose a novel framework that uses federated machine learning combined with Internet of Things technologies, for forest fire ignition classification prediction. The framework incorporates distributed IoT weather stations deployed in an area prone to forest fires. We find comparable prediction accuracy between a federated machine learning system and a central server machine learning system. Our federated system, trained on an imbalanced dataset comprising 5,008,365 non-ignition cases and 45,411 ignition instances, has shown encouraging outcomes. It attained an Accuracy of around 0.76 and a ROC-AUC of about 0.80. The performance is on par with other systems, indicating that our approach is effective in classifying forest fire ignitions with a spatial resolution markedly superior to that of centralized systems.

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Hydrodynamic Response of Mocean Wave Energy Converter in Extreme Waves 15th

Hydrodynamic Response of Mocean Wave Energy Converter in Ext...

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15th European Wave and Tidal Energy Conference, EWTEC 2023

作者： Samuel, J.A. Venugopal, V. Retzler, C.H. Ma, Q. School of Natural and Built Environment Queen’s University Belfast United Kingdom Institute for Energy Systems School of Engineering The University of Edinburgh United Kingdom Mocean Energy Ltd United Kingdom School of Mathematics Computer Science and Engineering City University of London United Kingdom

The design of moored floating wave energy converters (WECs) must take into account extreme responses and mooring line loads in order to ensure their survival and continued wave power generation in the ocean environment. This study focuses on Mocean Energy’s Blue Horizon C1 hinged raft WEC and aims to provide a better understanding of its hydrodynamic characteristics in survival wave conditions. To achieve this, a physical model study was conducted on a Froude scale of 1 in 50 at the FloWave Ocean Energy Research Facility, University of Edinburgh. The experiments involved the use of NewWaves focusing of crest and trough at the model hinge location, as well as long crested irregular waves. Motion responses of the fore and aft bodies of the WEC were measured using a Qualisys camera, and single component load cells were used to measure the forces in the 3-point catenary mooring lines. The hydrodynamic characteristics of the WEC were evaluated in terms of response amplitude operators and non-dimensional mooring line loads. Results indicate that the fore and aft bodies of the WEC exhibit similar motion responses, except for the pitch motion. The aft body has a pitch response 2 to 3 times higher than the fore body. Concerning the moorings, the wave load on the mooring line in line with the wave direction was found to be higher than the other two mooring lines which were arranged at an angle to the wave direction. The results will provide insight into the behaviour of the Mocean device in survival wave conditions and will aid with the determination of appropriate design parameters for optimal performance and survival in the ocean environment. © 2023 European Wave and Tidal Energy Conference. This paper has been subjected to single-blind peer review.

关键词： Mooring cables

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