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13th international conference on Bioinformatics and Biomedical Science, ICBBS 2024

作者： Chen, Ying Luo, Jinsong Liang, Deyi Xie, Yewei Zhou, Yi Wu, Dan Cheng, Weibin Tang, Weiming Jing, Fengshi Faculty of Data Science City University of Macau China School of Automation Central South University Hunan Changsha China Institute for Hospital Management Tsinghua University Guangdong Shenzhen China Guangdong Women and Children's Hospital Guangdong Guangzhou China Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Singapore Zhuhai Center for Disease Control and Prevention Guangdong Zhuhai China School of Public Health Nanjing Medical University Jiangsu Nanjing China School of Data Science City University of Hong Kong Hong Kong Hong Kong Guangdong Second Provincial General Hospital Guangdong Guangzhou China School of Medicine University of North Carolina at Chapel Hill Chapel HillNC United States

ISBN: (纸本)9798400717956

This study investigates the predictive utility of Google search queries for forecasting influenza-like illness (ILI) in compulsory education schools in Macau. The increasing availability of online data offers a novel approach to health surveillance, potentially improving the timeliness and accuracy of ILI predictions in educational settings. We employed three machine learning models: extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and ridge regression (Ridge), to forecast the ILI-caused absence rate in kindergartens, primary schools, and middle schools one and two weeks in advance in Macau. The covariates for these models include Google search queries and historical ILI data. This is Macau's first study to apply machine learning methodologies and Internet big data to the surveillance of ILI in educational institutions. Our approach offers a feasible and computationally efficient method for forecasting ILI in Macau's compulsory education schools. This methodology could be adapted for use in other regions with limited influenza data resources, providing a valuable tool for public health planning and response. © 2024 Copyright held by the owner/author(s).

关键词： Biomedical data analysis

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Using Diffusion Models for data Augmentation on Limited Rodent OCT datasets 25th

Using Diffusion Models for Data Augmentation on Limited Rode...

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25th international conference on Intelligent data engineering and Automated learning

作者： Garcia-Torres, Fernando del Amor, Roefo Morales-Martinez, Sandra Barroso, Alvaro Kemper, Bjoern Schnekeriburger, Jurgen Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Centrada Ser Humano Valencia Spain Univ Munster Biomed Technol Ctr Med Fac Mendelstr 17 D-48149 Munster Germany

ISBN: (纸本)9783031777301;9783031777318

Optical coherence tomography (OCT) has transformed ophthalmology by enabling accurate diagnosis of retinal diseases. OCT in rodents is used in research studies of ophthalmic diseases and treatment evaluation. However, the scarcity of data limits the effectiveness of deep learning models for this purpose. This study explores the use of synthetic data generation to improve deep learning models for OCT image analysis from rodent retinas. To address this, the research leverages advanced class-conditional generative models to create high-quality synthetic images of ex-vivo rodent retinas embedded in two different mediums (solid resin and liquid media) with a reduced initial database. The conditional Denoising Diffusion Probabilistic Model (DDPM) proved to be the best model, as it produces more realistic images that closely mimic the characteristics of real rodent retinal scans. By augmenting the training datasets with these synthetic images, the performance of deep learning models in segmenting retinal layers improved by 7%. This approach reduces reliance on scarce biological samples and animal testing, promoting ethical research practices while improving the accuracy and efficiency of AI-driven medical diagnostics. The integration of synthetic data generation represents a significant advance in medical imaging, offering a scalable solution to data scarcity, generating reliable samples in two mediums and promoting the development of more robust and accurate diagnostic tools.

关键词： Synthetic data generation Deep learning Optical coherence tomography Image analysis Retinal diseases Denoising Diffusion Probabilistic Models Medical imaging

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MMA-Net: Multi-Modal Attention Network for 2-D Object Detection in Autonomous Driving

MMA-Net: Multi-Modal Attention Network for 2-D Object Detect...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gaur, Abhilash Goel, Shubh Goel, Kanishk Srirangarajan, Seshan Tseng, Po-Hsuan Feng, Kai-Ten New Delhi India Hsinchu Taiwan The Department of Electrical Engineering IIT Delhi New Delhi India The Department of Electrical Engineering Bharti School of Telecommunication Technology and Management IIT Delhi New Delhi India The Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan The Department of Electronics and Electrical Engineering NYCU Hsinchu Taiwan

ISBN: (纸本)9798350368741

Autonomous driving technology relies heavily on sensor data for environment perception. Heterogeneous sensors such as lidar, radar, and camera have their own strengths and limitations. Therefore, relying on any single sensor would restrict the effectiveness of autonomous driving technology. However, integrating data from such heterogeneous sensors poses challenges due to differences in their representations. This article outlines a deep learning network aimed at designing modality-agnostic multi-modal fusion architecture. We study sensor data from different modalities and learn fine-grained representations using modality-specific feature encoders independently. Then, a multi-modal attention-based network (MMA-Net) is proposed to fuse the data from heterogeneous modalities. The proposed MMA-Net fuses multi-modal sensor data by jointly exploiting the inter-modality and intra-modality relationships among camera, lidar, and radar sensors. The effectiveness of the proposed multi-modal fusion architecture is demonstrated using 2-D object detection metrics through extensive experiments on a dataset generated using the CARLA simulator. © 2025 IEEE.

关键词： autonomous driving CARLA simulator multi-modal learning object detection Sensor fusion

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HID-NAS: A Novel Neural Architecture Search Pipeline for High Information Density data

HID-NAS: A Novel Neural Architecture Search Pipeline for Hig...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Wenbo Deng, Tao Yan, Fei Southwest Jiaotong University Chengdu China

ISBN: (纸本)9798350368741

Neural Architecture Search (NAS) is a core component of automated machine learning, enabling the automatic discovery of task-specific architectures. However, directly employing raw data for NAS faces challenges in terms of large data volume, low search efficiency, and potential data privacy concerns. To address these limitations, we condense large-scale target datasets into high information-density proxy datasets. And we propose a novel pipeline, HID-NAS, which skillfully exploits High Information Density(HID) data for efficient neural architecture search. At the algorithmic level, our algorithm focuses on the inherent problem of DARTS-derived algorithms, i.e., the discrepancy between trained supernet and selected architectures. We pioneer the phenomenon of"Selection Error" that occurs during architecture selection and introduce an attention mechanism that focuses on candidate edges to improve the accuracy of pipeline-selected architectures. Furthermore, we also formulate a regularization mechanism that employs the high information density data of the proxy dataset to transform the architectural parameter updates into an adversarial game and adjust the strengths through a normalization mechanism. These three key components - attention, regularization, and normalization - allow our pipeline to efficiently identify high-quality models using the distilled dataset. Experimental results demonstrate a significant reduction in search time, achieving high-quality models within just two minutes. © 2025 IEEE.

关键词： Automated machine learning dataset distillation differential architecture search selection error

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A Critical Review on Visual Attribute learning in the AEC Industry

A Critical Review on Visual Attribute Learning in the AEC In...

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2024 international conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Deng, Hui Zhang, Hengyun Fu, Kejie Deng, Yichuan Zhu, Huanyu National Key Laboratory of Subtropical Architecture and Urban Science School of Civil Engineering and Transportation South China Univ. of Technology Guangzhou China School of Civil Engineering and Transportation South China Univ. of Technology Guangzhou China School of Civil and Transportation Engineering Guangdong Univ. of Technology Guangzhou China South China Univ. of Technology Guangzhou China

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

Visual attributes, as middle-level semantics connecting the underlying visual information and the advanced visual semantics, are considered to be an effective way to bridge the "semantic gap" between the high-level semantics of building project management and the underlying visual data information. This paper provides a critical review of the research on visual attribute learning in the construction industry, with literature data selected from the Web of Science (WOS) database, and reveals the direction of research on visual attributes in the construction industry and its evolutionary trends through quantitative and qualitative analysis. This paper aims to offer theoretical support for the technical implementation that follows, as well as aid in comprehending construction scenarios and intelligent building management. © ASCE.

关键词： Project management

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The 4th international conference on data Science, machine learning 2022

The 4th International Conference on Data Science, Machine Le...

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The 4th international conference on data Science, machine learning 2022

ISBN: (纸本)9789819920570

The proceedings contain 39 papers. The special focus in this conference is on data Science, machine learning and Applications. The topics include: Monitoring the Farming Conditions Using IoT;Design of OTFS Modulation by Superimposed Pilot-Based Channel Estimation and Embedded Pilot-Aided Estimation;Morphology and EMD-Based Patch-Wise Image Fusion;PSO-Based Evolutionary Image Segmentation System for Analysis of Fatty Liver Level Recognition;blake–Zisserman Model of Segmentation Method for Low-Contrast and Piecewise Smooth Image;Performance Enhancement of Wireless Systems Using Hybrid RIS Technique;Creation of a Platform for Artisans to Promote Their Product Using Blockchain as NFT;face Recognition Based Home Security System to Detect Usual/Unusual person Using IoT;opinion Mining-Based Fake Review Detection Using Deep learning Technique;automatic Detection and Cleaning of Manhole Blockages Using IoT;design and Implementation of a Smart Door Locking System with Automatic Appliance Switching;dynamic Game Difficulty Adjustment Based on Facial Emotion Recognition;parallel Implementation of PageRank Based K-Means Clustering on a Multithreaded Architecture;ZACube-2 Mission Operations Analysis;a Novel Approach for Speech Emotion Recognition with Facial Expression Analysis;effect of Environment on Students Performance Through Orange Tool of data Mining;phishing Email Mitigation Technique Using Back-Propagation Neural Network for Cyber Space;crime Visualization and Forecasting Using machine learning;an Empirical Comparison of Classification machine learning Models Using Medical datasets;Analysis of the SEER data set for Lung Cancer Diagnosis for Stage Classification and Survival Analysis;risk Prediction of Chronic Kidney Disease Using machine learning Algorithms;sandalwood Tree Deduction Using Deep learning;phishing Attack Detection Using machine learning.

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Analysis of the Life Insurance Business Performance Based on COVID by Using machine learning Algorithms

Analysis of the Life Insurance Business Performance Based on...

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international conference on machine learning and Big data Analytics, ICMLBDA 2023

作者： Nithya, P. Nandakumar, C.D. Srinivasan, S. Department of Mathematics & Actuarial Science B.S. Abdur Rahman Crescent Institute of Science & Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031513374

COVID-19 has had a worldwide impact. Economically, it has a ripple effect on the entire financial market. COVID has caused significant problems for the majority of insurance companies. COVID had a substantial impact on the life insurance industry. Most people nowadays have both life and health insurance based on how well that insurance company is performing. Because the majority of them purchased insurance, the company’s share capital and risk are both increasing (Nti et al. Artif Intell Rev 53(4):3007–3057, 2020). As the share price of the insurance company fluctuates, insurance uses a hedging concept that minimizes risk while maximizing profit. Because the share price is volatile, it is difficult to forecast. Stock prediction is one of the most difficult tasks in artificial intelligence (Nti et al. J Big data 7(1), 2020). Consequently, using machine learning techniques, it is possible to forecast how the insurance industry will perform on the day of its closing price. We collected data from Yahoo Finance for HDFC Life Insurance, Star Health, and Allied Insurance, SBI Life Insurance, ICICI Prudential Life Insurance, and Life Insurance Corporation of India from January 1, 2018, to December 31, 2022. The methodology of work is carried out using various models of machine-learning algorithms. The algorithms used to analyze company performance are Bayesian ridge regression, support vector machine, random forest regression, multinomial Naive Bayes, XGBoost regression, and Bayesian ridge regression. By employing these algorithms to analyze future share prices and boost the accuracy of stock price predictions (Nti et al. Open Comput Sci 10:153–163, 2020), investors can use the analysis to make investments in the company as well as buy insurance policies. They can also determine the most suitable forecasting models to produce the best estimations with the least amount of error (Nti et al. Appl Comput Syst 25(1):33–42, 2020). © The Author(s), under exclusive license to Sprin

关键词： Adversarial machine learning

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Solving numerical and engineering optimization problems using a dynamic dual-population differential evolution algorithm

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international JOURNAL OF machine learning AND CYBERNETICS 2025年第3期16卷 1701-1760页

作者： Zuo, Wenlu Gao, Yuelin North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Peoples R China Ningxia Prov Key Lab Intelligent Informat & Data P Yinchuan 750021 Peoples R China

Differential evolution (DE) is a cutting-edge meta-heuristic algorithm known for its simplicity and low computational overhead. But the traditional DE cannot effectively balance between exploration and exploitation. To solve this problem, in this paper, a dynamic dual-population DE variant (ADPDE) is proposed. Firstly, the dynamic population division mechanism based on individual potential value is presented to divide the population into two subgroups, effectively improving the population diversity. Secondly, a nonlinear reduction mechanism is designed to dynamically adjust the size of potential subgroup to allocate computing resources reasonably. Thirdly, two unique mutation strategies are adopted for two subgroups respectively to better utilise the effective information of potential individuals and ensure fast convergence speed. Finally, adaptive parameter setting methods of two subgroups further achieve the balance between exploration and exploitation. The effectiveness of improved strategies is verified on 21 classical benchmark functions. Then, to verify the overall performance of ADPDE, it is compared with three standard DE algorithms, eight excellent DE variants and seven advanced evolutionary algorithms on CEC2013, CEC2017 and CEC2020 test suites, respectively, and the results show that ADPDE has higher accuracy and faster convergence speed. Furthermore, ADPDE is compared with eight well-known optimizers and CEC2020 winner algorithms on nine real-world engineering optimization problems, and the results indicate ADPDE has the development potential for constrained optimization problems as well.

关键词： Differential evolution Dual-population Individual potential value engineering optimization

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Multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel dataset and Deep-learning Experiments

Multimodality for Diagnosis of Asian Choroidal Vasculopath...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Cho, Daehyun Kim, Young Ho Ahn, Somin Oh, Jaeryung Wallraven, Christian Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Ophthalmology Korea University College of Medicine Seoul Korea Republic of Department of Ophthalmology Dongguk University Ilsan Hospital Goyang Korea Republic of Department of Artificial Intelligence and Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9783031845246

Deep learning algorithms show tremendous potential for clinical decision-making - for example, in providing automated diagnoses of imaging data. However, typical clinical datasets often are limited in size, modalities, and contain heterogeneous, incomplete data, which presents challenges for deep learning frameworks that necessitate larger, uniform datasets, complicating their deployment especially with new types of disease models. In this work, we present a case study for deep learning in such a challenging setting in the context of diagnosing Asian choroidal Vasculopathy (ACV), which is a retinopathy profile currently under discussion in ophthalmology to be differentiated from age-related macular degeneration (AMD). We first introduce a novel, human-annotated multimodal dataset for ACV versus AMD diagnosis incorporating four different imaging modalities. We next explore the usefulness of "foundation models" for this data, compared to traditional dataset-specific training. Most importantly, we investigate which of the four modalities is most discriminative and whether bi-modal classification is able to enhance performance across multiple fusion approaches. We also discuss first results of salient features using explainability techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Optimising Neural Network Training Efficiency Through Spectral Parameter-Based Multiple Adaptive learning Rates 24

Optimising Neural Network Training Efficiency Through Spectr...

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7th international conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Koay, Yeong Lin Sim, Hong Seng Goh, Yong Kheng Chua, Sing Yee Leong, Wah June Centre for Mathematical Sciences Universiti Tunku Abdul Rahman Selangor Kajang Malaysia Department of Electrical and Electronics Engineering Universiti Tunku Abdul Rahman Selangor Kajang Malaysia Institute for Mathematical Research Universiti Putra Malaysia Selangor Serdang Malaysia

ISBN: (纸本)9798400717437

The process of training neural networks heavily involves solving optimization problems. Most optimization algorithms use a fixed learning rate or a simplified adaptive updating scheme in every iteration. In this paper, we propose a stochastic gradient descent method with multiple adaptive learning rates (MAdaGrad) and Adam with multiple adaptive learning rates (MAdaGrad Adam). The proposed algorithm updates the learning rate in every iteration based on the approximated spectrum of the Hessian of the loss function. The method is compared to the existing optimization methods in machine learning, namely stochastic gradient descent method (SGD) and Adam. Selected datasets are used to evaluate the performance of the proposed method. The proposed algorithm is used to train the neural networks with different hidden layer sizes and different neurons. The numerical results prove that the proposed methods perform better than SGD and Adam. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Contrastive learning

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