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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Tran, Triet M. Tran, Thi T. Khanmohammadi, Sina University of Oklahoma Data Science And Analytics Institute NormanOK73109 United States University of Oklahoma School of Computer Science NormanOK73109 United States

ISBN: (纸本)9798350371499

Surgical resection is one of the main treatment options for brain tumors. However, there is a risk of postoperative cognitive deterioration associated with resective surgery. Recent studies suggest that pre-surgery brain dynamics captured using functional Magnetic Resonance Imaging (fMRI) could provide valuable information about the risk of post-surgery cognitive decline. However, most of these studies are based on simple regression analysis of the raw fMRI signals that do not capture the underlying complex brain dynamics. Here, we investigated the role of higher-order functional brain networks in predicting cognitive decline after surgical resection of brain tumors. More specifically, we looked at the predictive power of second-order functional brain networks in estimating post-surgery working memory (WM) performance. Our results show that the second-order functional brain networks can accurately predict the working memory decline in patients with glioma and meningioma tumors. These findings suggest that there is an interesting relationship between pre-surgical higher-order brain dynamics and the risk of cognitive decline after surgery, which could potentially yield a better prognostic marker for treatment planning of brain tumor patients. © 2024 IEEE.

关键词： Transplantation (surgical)

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Reconstructing Children’s Faces from Cropped Lip and Nose Images: A Large Mask Inpainting Approach for Privacy-Conscious Cleft data 5th

Reconstructing Children’s Faces from Cropped Lip and Nose...

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5th International Conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Atputharuban, Daniel Anojan Theopold, Christoph Lawlor, Aonghus The Insight Centre for Data Analytics School of Computer Science University College Dublin Dublin Ireland Children’s Health Ireland at Temple Street Dublin Ireland

ISBN: (纸本)9789819638628

Cleft lip and Palate (CLP) is a congenital anomaly that leads to deformities in the orofacial regions at birth. While Cleft deformities can be fully corrected through primary Cleft repair surgery followed by secondary interventions, accurately representing the outcomes of primary Cleft repair is crucial for planning subsequent treatments. However, current surgical assessment methods are subjective and often result in discrepancies among clinicians. Although deep learning solutions have been explored as alternatives for providing objective measurements, the limited availability of Cleft data—primarily due to privacy concerns—raises concerns about their accuracy in real-world applications. Transfer learning with pretrained facial deep learning models, which are typically trained on full-face images, cannot be directly applied to Cleft conditions because clinical Cleft datasets generally consist of cropped facial images focusing on Cleft-prone areas, lacking full facial representations. To address this challenge, we propose a pipeline that generates synthetic full-face images of children from these cropped Cleft prone facial regions. The proposed inpainting model preserves Cleft-prone regions while generating semantically appealing whole face regions with continuity. These synthetic images serve as a valuable resource for analyzing anomalies using pretrained facial deep learning models trained on whole faces, which have demonstrated success in identifying fine and subtle facial details. Additionally, our approach enhances patient privacy and mitigates risks associated with model inversion attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Neural Machine Translation Models with Attention-Based Dropout Layer

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Computers, Materials & Continua 2023年第5期75卷 2981-3009页

作者： Huma Israr Safdar Abbas Khan Muhammad Ali Tahir Muhammad Khuram Shahzad Muneer Ahmad Jasni Mohamad Zain School of Electrical Engineering and Computer Science(SEECS) National University of Science and TechnologyIslamabadPakistan Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA40450Shah AlamSelangorMalaysia

In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of *** model has obtained state-of-the-art performance for several language ***,there has been little work exploring useful architectures for Urdu-to-English machine *** conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and *** results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen *** trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,*** a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more *** attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear ***,we considered refining the attention-based models by defining an additional attention-based dropout *** dropout fixes alignment errors and minimizes translation errors at the word *** empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation *** ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as *** empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.

关键词： Natural language processing neural machine translation word embedding attention perplexity selective dropout regularization Urdu Persian Arabic BLEU

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Uncertainty-Aware Optimisation for Sustainable Multimedia Event Processing in Big data Streams

Uncertainty-Aware Optimisation for Sustainable Multimedia Ev...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Pontes, Felipe Arruda Schukat, Michael Curry, Edward University of Galway Insight Centre for Data Analytics Galway Ireland University of Galway School of Computer Science Galway Ireland

ISBN: (纸本)9798350324457

Multimedia Event Processing (MEP) systems play a critical role in various Internet of Things (IoT) applications, including Smart Cities and Health and Safety, by processing large amounts of multimedia data streams. These systems often leverage state-of-the-art Deep Neural Network (DNN) models to enhance their capabilities. However, the growth of Cloud and Edge Big data applications has imposed a significant environ-mental burden, further intensified by the substantial energy consumption associated with certain DNN model *** study addresses the environmental impact of growing Big data stream applications and focuses on optimising MEP systems to mitigate this issue. We tackle uncertainties arising from user-defined Quality of Service (QoS) interpretations and service worker measurement imprecisions by using uncertainty-aware solutions for the service selection problem in order to improve the QoS within MEP *** results reveal substantial advantages in employing uncertainty-aware strategies. These approaches consistently enhance QoS metrics, outperforming their uncertainty-oblivious counterparts. Specifically, we report improvements in more than 67%, 69%, and 20% of the scenarios, on average, for energy consumption, latency, and accuracy, respectively. These enhancements become evident within just three hours of processing, resulting in energy savings of up to 1.2 kilowatt-hours and latency reductions of 213 seconds, with a 0.29% average loss in query accuracy. These strategies improve system efficiency and ecological sustainability while incurring a small accuracy trade-off. When extrapolated over a year, the environmental benefits become even more noticeable, surpassing the energy requirements for a 1000 Km electric vehicle round-trip from Amsterdam to Paris and back. © 2023 IEEE.

关键词： Big data Deep Neural Networks Multimedia Streaming Sustainability

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Multi-Objective Mixed Bus Fleet Charging Schedule Problem with Time-of-Use for Real-world data-sets 32

Multi-Objective Mixed Bus Fleet Charging Schedule Problem wi...

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32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Jarvis, Padraigh Climent, Laura Arbelaez, Alejandro Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland Universitat Politècnica de València Valencia Spain

As the effects of climate change are increasingly felt worldwide, the transition to Electric Buses (EB) presents an opportunity to decarbonize the transportation sector. Several issues exist that hinder the adoption of a green bus fleet. Such as the increased cost, reduced travel distances, and required recharge times, which may negatively impact service quality. This work proposes a Mixed Integer Programming model to solve a multi-objective mixed fleet charging schedule problem. The mixed fleet considers EBs and Internal Combustion Engine Buses (ICEBs) and minimizes daily costs, such as fuel price, the Social Cost of Carbon (SCC) produced by the bus fleet, and the Value of Time (VoT) of public transport users. Non-linear charging is considered as well as alternative approaches along with Time of Use (TOU) constraints for electricity price and SCC. Empirical evaluation shows that significant savings can be made, with reductions of over 20000 in fuel costs, and reductions of over 100 tCO2eq per day. Consideration of VoT minimizes negative customer impact, limiting late arrivals to an average of 8.78 seconds per EB. The inclusion of non-linear charging makes minimal positive impact compared to limiting the total capacity of the battery, and while the inclusion of TOU constraints correlates to more savings, the amount saved is minute. © 2024 Copyright for this paper by its authors.

关键词： Integer programming

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Influencing Factors of Healthy Aging Risk Assessed Using Biomarkers:A Life Course Perspective

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China CDC weekly 2024年第11期6卷 219-224,I0014-I0017页

作者： Cedric Zhang Bo Lua Yajie Gao Jinming Li Xingqi Cao Xinwei Lyu Yinuo Tu Shuyi Jin Zuyun Liu Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Healththe Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceZhejiang University School of MedicineHangzhou CityZhejiang ProvinceChina Institute of Epidemiology and Health Care University College LondonLondonUK College of Chemical and Biological Engineering Zhejiang UniversityHangzhou CityZhejiang ProvinceChina

Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of *** this study,we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English *** studies have predominantly used single biomarkers,such as C-reactive protein,or focused on specific life course stages and factors such as socioeconomic status,mental health,educational levels,and unhealthy *** summarizing the progress in this field,our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.

关键词： such insight directions

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Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases 30th

Can We Transfer Noise Patterns? A Multi-environment Spectru...

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30th International Conference on Neural Information Processing, ICONIP 2023

作者： Du, Haiwen Ju, Zheng An, Yu Du, Honghui Zhu, Dongjie Tian, Zhaoshuo Lawlor, Aonghus Dong, Ruihai School of Astronautics Harbin Institute of Technology Harbin China Insight Centre for Data Analytics Dublin Ireland School of Computer Science and Technology Harbin Institute of Technology Weihai China

ISBN: (纸本)9789819981830

Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer from complex noise patterns when deployed in non-laboratory environments. To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples. Unfortunately, the inevitable sample-level baseline noise makes the model unable to obtain the paired data that only differ in dataset-level environmental noise. To address the problem, we generate a sample-to-sample case-base to exclude the interference of sample-level noise on dataset-level noise learning, enhancing the system’s learning performance. Experiments on spectral data with different background noises demonstrate the good noise-transferring ability of the proposed method against baseline systems ranging from wavelet denoising, deep neural networks, and generative models. From this research, we posit that our method can enhance the performance of DL models by generating high-quality cases. The source code is made publicly available online at https://***/Magnomic/CNST. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spectrum analysis

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Strategic Blended Learning for Addressing Multiclass Imbalance in Classification

Strategic Blended Learning for Addressing Multiclass Imbalan...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Sridhar, S. Anusuya, S. SRM Institute of Science and Technology Department of CSE Emerging Technologies TN Chennai India Saveetha School of Engineering Department of Data Analytics SIMATS TN Chennai India

ISBN: (纸本)9798331512965

Numerous practical applications involve multiclass classification challenges, where the spread of data across the classes is frequently uneven. Due to this inherent data imbalance, supervised learning models often exhibit a bias toward classifying instances into the category with the highest number of data points, posing a significant problem that requires attention. In the realm of classification of data with multiple classes and imbalanced distributions, researchers in machine learning persistently endeavor to mitigate the classifier's inclination towards dominant class with more sample size. Existing studies aim to alleviate this inclination by alternatively adjusting the data for balance prior to the classifier's learning process or adjusting the classifier's training process to focus more on rare class having least number of samples, or employing hybrid strategies. Existing learning approaches encounter challenges in delineating clear boundaries between samples of classes resulting from an imbalanced class distribution and issues with class overlapping. Consequently, the recognition rate for minority classes is hindered. A novel Strategic Blended Learning (SBL) approach is introduced, employing dual decision trees. The first decision tree is utilized to generate an induced dataset through a grouping approach, while the second decision tree is employed to train and make decisions guided by the induced dataset. A noteworthy aspect of this algorithmic approach is its ability to identify rare data points without affecting the inherent distribution of data. To evaluate the proposed algorithm, various multiclass imbalanced datasets from UCI were employed for classification. The findings revealed that the Strategic Blended Learning method excels in detecting both rare and dominant category samples. © 2025 IEEE.

关键词： Contrastive Learning

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Research on Large-scale Structured and Unstructured data Processing based on Large Language Model 24

Research on Large-scale Structured and Unstructured Data Pro...

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2024 International Conference on Machine Learning, Pattern Recognition and Automation Engineering, MLPRAE 2024

作者： Li, Bohang Jiang, Gaozhe Li, Ningxin Song, Chaoda Department of Data Science Shopee Pte. Ltd. Singapore Institute of Operations Research and Analytics National University of Singapore Singapore Fu Foundation School of Engineering and Applied Science Columbia University United States School of Engineering Department of Computer and Data Sciences United States

ISBN: (纸本)9798400709876

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types. © 2024 ACM.

关键词： data accuracy

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Intelligent Pavement Condition Rating System for Cycle Routes and Greenways 11

Intelligent Pavement Condition Rating System for Cycle Route...

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11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Haider Shah, Syed M. Qureshi, Waqar Shahid O’ Dea, Gerard Ullah, Ihsan Insigth SFI Research Center for Data Analytics University of Galway Galway Ireland School of Computer Science University of Galway Galway Ireland Transport Infrastructure Ireland Ireland

ISBN: (纸本)9789897587450

This study introduces an intelligent framework for assessing cycling infrastructure, addressing the limitations of traditional pavement evaluation methods. At the core of the system is the CRSI, a 1-to-5 rating scale specifically designed to evaluate cycle routes based on critical factors like surface quality, vegetation encroachment, and drainage. A dataset of over 40,000 frames, extracted from videos captured using handlebar-mounted GoPro cameras and annotated by experts, forms the foundation of the system. Four deep learning (DL) models LeNet, AlexNet, EfficientNet-B2, and Swin Transformer-Tiny were trained and evaluated for Cycle Route Surface Index (CRSI) classification. Among all models, Swin Transformer-Tiny performed the best, achieving an impressive accuracy of 99.90%. To further test its robustness, we evaluated the system on four new videos, from which four separate frame sets were generated. Among these, Swin Transformer-Tiny again delivered the highest accuracy, reaching 86.67%, confirming its reliability across different datasets. This CRSI-based framework provides a scalable, automated solution for evaluating cycling infrastructure, empowering transportation agencies to improve maintenance and ensure safer, more accessible cycling networks. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Convolutional neural networks

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