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multimodal machine learning Guides Low Carbon Aeration Strategies in Urban Wastewater Treatment

Engineering 2024年第5期36卷 51-62页

作者： Hong-Cheng Wang Yu-Qi Wang Xu Wang Wan-Xin Yin Ting-Chao Yu Chen-Hao Xue Ai-Jie Wang State Key Laboratory of Urban Water Resource and Environment School of Civil and Environmental EngineeringHarbin Institute of Technology(Shenzhen)Shenzhen 518055China Key Laboratory of Drinking Water Safety and Distribution Technology of Zhejiang Province College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058China

The potential for reducing greenhouse gas(GHG)emissions and energy consumption in wastewater treatment can be realized through intelligent control,with machine learning(ML)and multimodality emerging as a promising ***,we introduce an ML technique based on multimodal strategies,focusing specifically on intelligent aeration control in wastewater treatment plants(WWTPs).The generalization of the multimodal strategy is demonstrated on eight ML *** results demonstrate that this multimodal strategy significantly enhances model indicators for ML in environmental science and the efficiency of aeration control,exhibiting exceptional performance and *** random forest with visual models achieves the highest accuracy in forecasting aeration quantity in multimodal models,with a mean absolute percentage error of 4.4%and a coefficient of determination of *** testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8%compared to traditional fuzzy control *** potential application of these strategies in critical water science domains is *** foster accessibility and promote widespread adoption,the multimodal ML models are freely available on GitHub,thereby eliminating technical barriers and encouraging the application of artificial intelligence in urban wastewater treatment.

关键词： Wastewater treatment multimodal machine learning Deep learning Aeration control Interpretable machine learning

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multimodal machine learning-Based Ductal Carcinoma in situ Prediction from Breast Fibromatosis

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CANCER MANAGEMENT AND RESEARCH 2024年 16卷 811-823页

作者： Jiang, Yan Peng, Yuanyuan Wu, Yingyi Sun, Qing Hua, Tebo Ningbo Med Ctr Lihuili Hosp Dept Ultrasound Ningbo Zhejiang Peoples R China Ningbo Med Ctr Lihuili Hosp Dept Thyroid & Breast Surg Ningbo 315040 Zhejiang Peoples R China

Objective: To develop a clinical-radiomics model using a multimodal machine learning method for distinguishing ductal carcinoma in situ (DCIS) from breast fibromatosis. Methods: The clinical factors, ultrasound features, and related ultrasound images of 306 patients (198 DCIS patients) were retrospectively collected. Patients in the development and validation cohort were 184 and 122, respectively. The independent clinical and ultrasound factors identified by the multivariable logistic regression analysis were used for the clinical-ultrasound model construction. Then, the region of interest of breast lesions was delineated and radiomics features were extracted. Six machine learning algorithms were trained to develop a radiomics model. The algorithm with higher and more stable prediction ability was chosen to convert the output of the results into the Radscore. Further, the independent clinical predictors and Radscore were enrolled into the logistic regression analysis to generate a combined clinical-radiomics model. The receiver operating characteristic curve analysis, DeLong test, and decision curve analysis were adopted to compare the prediction ability and clinical efficacy of three different models. Results: Among the six classifiers, logistic regression model was selected as the final radiomics model. Besides, the combined clinical-radiomics model exhibited a superior ability in distinguishing DCIS from breast fibromatosis to the clinical-ultrasound model and the radiomics model. Conclusion: The combined model by integrating clinical-ultrasound factors and radiomics features performed well in predicting DCIS, which might promote prompt interventions to improve the early diagnosis and prognosis of the patients.

关键词： multimodal machine learning clinical-ultrasound features radiomics ductal carcinoma in situ

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multimodal machine learning for language and speech markers identification in mental health

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2024年第1期24卷 1-20页

作者： Drougkas, Georgios Bakker, Erwin M. Spruit, Marco Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Leiden Univ Med Ctr Publ Hlth & Primary Care Leiden Netherlands

BackgroundThere are numerous papers focusing on diagnosing mental health disorders using unimodal and multimodal approaches. However, our literature review shows that the majority of these studies either use unimodal approaches to diagnose a variety of mental disorders or employ multimodal approaches to diagnose a single mental disorder instead. In this research we combine these approaches by first identifying and compiling an extensive list of mental health disorder markers for a wide range of mental illnesses which have been used for both unimodal and multimodal methods, which is subsequently used for determining whether the multimodal approach can outperform the unimodal *** this study we used the well known and robust multimodal DAIC-WOZ dataset derived from clinical interviews. Here we focus on the modalities text and audio. First, we constructed two unimodal models to analyze text and audio data, respectively, using feature extraction, based on the extensive list of mental disorder markers that has been identified and compiled by us using related and earlier studies. For our unimodal text model, we also propose an initial pragmatic binary label creation process. Then, we employed an early fusion strategy to combine our text and audio features before model processing. Our fused feature set was then given as input to various baseline machine and deep learning algorithms, including Support Vector machines, Logistic Regressions, Random Forests, and fully connected neural network classifiers (Dense Layers). Ultimately, the performance of our models was evaluated using accuracy, AUC-ROC score, and two F1 metrics: one for the prediction of positive cases and one for the prediction of negative ***, the unimodal text models achieved an accuracy ranging from 78% to 87% and an AUC-ROC score between 85% and 93%, while the unimodal audio models attained an accuracy of 64% to 72% and AUC-ROC scores of 53% to 75%. The experimental results i

关键词： Mental illness marker Mental health disorder multimodal machine learning Unimodal model Early fusion Textual features extraction Acoustic features extraction Feature selection Binary labels creation

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Integrating geological model via A multimodal machine learning approach in shale gas production forecast

GAS SCIENCE AND ENGINEERING

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GAS SCIENCE AND ENGINEERING 2025年 139卷

作者： Wang, Muming Zhang, Xialin Wang, Hai Hui, Gang Chen, Shengnan Univ Calgary Dept Chem & Petr Engn Calgary AB Canada Res Inst Petr Explorat & Dev Beijing Peoples R China China Univ Petr State Key Lab Petr Resources & Prospecting Beijing Peoples R China

machine learning (ML) has achieved great success in production prediction for unconventional shale gas reservoirs. However, these methods mostly rely on the discrete data collected from the wells, such as drilling, completion, and production data. In this study, a multimodal ML approach is proposed to incorporate not only the aforementioned tabular data but also the geological property distribution maps surrounding the production wells. More specifically, a visual parameterization method was applied to preprocess the unstructured data from a 3D geological model to account for the geology properties near the horizontal wells. A comprehensive architecture for a multimodal model was then developed, assimilating a convolutional neural network (CNN) module, an artificial neural network (ANN) module, and a fusion module. The CNN module was established to process and extract high-level information from the visual dataset, while the ANN module was devised to learn from traditional tabular datasets. A fusion module combined and interacted with the data from both modalities. Results have shown that the proposed multimodal model achieved the highest testing R2 of 0.828 by integrating the formation maps with tabular datasets, compared to 0.736 from ANN. This is owing to the fact that two wells with similar porosity values measured at well sites could penetrate formations with different qualities along their thousand meters of lateral length. Visual feature analysis indicates that while integrating more property distribution maps generally increases model accuracy, considerable improvement (from 0.736 to 0.816) is achieved by solely incorporating porosity maps.

关键词： Unconventional reservoir Shale gas production Production forecast multimodal machine learning Property distribution map

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Review of multimodal machine learning approaches in healthcare

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INFORMATION FUSION 2025年 114卷

作者： Krones, Felix Marikkar, Umar Parsons, Guy Szmul, Adam Mahdi, Adam Univ Oxford Oxford Internet Inst Oxford England Univ Surrey Inst People Ctr AI Guildford England UCL Wellcome EPSRC Ctr Intervent & Surg Sci London England

machine learning methods in healthcare have traditionally focused on using data from a single modality, limiting their ability to effectively replicate the clinical practice of integrating multiple sources of information for improved decision making. Clinicians typically rely on a variety of data sources including patients' demographic information, laboratory data, vital signs and various imaging data modalities to make informed decisions and contextualise their findings. Recent advances in machine learning have facilitated the more efficient incorporation of multimodal data, resulting in applications that better represent the clinician's approach. Here, we provide an overview of multimodal machine learning approaches in healthcare, encompassing various data modalities commonly used in clinical diagnoses, such as imaging, text, time series and tabular data. We discuss key stages of model development, including pre-training, fine-tuning and evaluation. Additionally, we explore common data fusion approaches used in modelling, highlighting their advantages and performance challenges. An overview is provided of 17 multimodal clinical datasets with detailed description of the specific data modalities used in each dataset. Over 50 studies have been reviewed, with a predominant focus on the integration of imaging and tabular data. While multimodal techniques have shown potential in improving predictive accuracy across many healthcare areas, our review highlights that the effectiveness of a method is contingent upon the specific data and task at hand.

关键词： Data fusion Healthcare multimodal machine learning Deep learning

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multimodal machine learning for Mental Disorder Detection: A Scoping Review 27

Multimodal Machine Learning for Mental Disorder Detection: A...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Nguyen, Thuy Trinh Pham, Viet Hoang-Quoc Le, Duc-Trong Vu, Xuan-Son Deligianni, Fani Nguyen, Hoang D. School of Computing Science University of Glasgow United Kingdom University of Engineering and Technology Vietnam National University Viet Nam Department of Computing Science Umeå University Sweden School of Computer Science and Information Technology University College Cork Ireland

Recent advancements in machine learning and multimedia technologies have paved new ways for automatic medical diagnosis. In mental health, multimodal inputs such as visual and audible sensing data are promising to investigate the underlying mechanisms of many conditions, such as depression and bipolar disorders. With the increasing burden on healthcare systems, timely diagnosis of mental diseases using multiple modalities might benefit millions of people worldwide. This scoping review provides an exploratory overview of recent multimodal machine learning approaches for mental disorder screening. We also discuss a generalised end-to-end multimodal machine learning pipeline for future research and development of multimodal disease detection. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： bipolar disorders depression mental disorder diagnosis multimodal machine learning stress disorders

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Foundations and Trends in multimodal machine learning: Principles, Challenges, and Open Questions

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ACM COMPUTING SURVEYS 2024年第10期56卷 1页

作者： Liang, Paul Pu Zadeh, Amir Morency, Louis-Philippe Carnegie Mellon Univ Pittsburgh PA 15213 USA

multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this article is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining three key principles ofmodality heterogeneity, connections, and interactions that have driven subsequent innovations, and propose a taxonomy of six core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy.

关键词： multimodal machine learning representation learning data heterogeneity feature interactions language and vision multimedia

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Prediction of Anastomotic Leakage in Esophageal Cancer Surgery: A multimodal machine learning Model Integrating Imaging and Clinical Data

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ACADEMIC RADIOLOGY 2024年第12期31卷 4878-4885页

作者： Klontzas, Michail E. Ri, Motonari Koltsakis, Emmanouil Stenqvist, Erik Kalarakis, Georgios Bostrom, Erik Kechagias, Aristotelis Schizas, Dimitrios Rouvelas, Ioannis Tzortzakakis, Antonios Karolinska Inst Dept Clin Sci Intervent & Technol CLINTEC Div Radiol Stockholm Sweden Univ Hosp Heraklion Dept Med Imaging Iraklion Greece Fdn Res & Technol FORTH Inst Comp Sci Computat Biomed Lab Iraklion Crete Greece Univ Crete Sch Med Dept Radiol Voutes Campus Iraklion Greece Karolinska Inst Dept Clin Sci Intervent & Technol CLINTEC Div Surg & Oncol Stockholm Sweden Karolinska Univ Hosp Dept Upper Abdominal Dis Stockholm Sweden Karolinska Univ Hosp Dept Diagnost Radiol Stockholm Sweden Kanta Hame Cent Hosp Dept Digest Surg Hameenlinna 13530 Finland Natl & Kapodistrian Univ Athens Laikon Gen Hosp Dept Surg 1 Athens 11527 Greece Karolinska Univ Hosp Med Radiat Phys & Nucl Med Stockholm Sweden

Rationale and Objectives: Surgery in combination with chemo/radiotherapy is the standard treatment for locally advanced esophageal cancer. Even after the introduction of minimally invasive techniques, esophagectomy carries significant morbidity and mortality. One of the most common and feared complications of esophagectomy is anastomotic leakage (AL). Our work aimed to develop a multimodal machine-learning model combining CT-derived and clinical data for predicting AL following esophagectomy for esophageal cancer. Material and Methods: A total of 471 patients were prospectively included (Jan 2010-Dec 2022). Preoperative computed tomography (CT) was used to evaluate celia trunk stenosis and vessel calcification. Clinical variables, including demographics, disease stage, operation details, postoperative CRP, and stage, were combined with CT data to build a model for AL prediction. Data was split into 80%:20% for training and testing, and an XGBoost model was developed with 10-fold cross-validation and early stopping. ROC curves and respective areas under the curve (AUC), sensitivity, specificity, PPV, NPV, and F1-scores were calculated. Results: A total of 117 patients (24.8%) exhibited post-operative AL. The XGboost model achieved an AUC of 79.2% (95%CI 69%-89.4%) with a specificity of 77.46%, a sensitivity of 65.22%, PPV of 48.39%, NPV of 87.3%, and F1-score of 56%. Shapley Additive exPlanation analysis showed the effect of individual variables on the result of the model. Decision curve analysis showed that the model was particularly beneficial for threshold probabilities between 15% and 48%. Conclusion: A clinically relevant multimodal model can predict AL, which is especially valuable in cases with low clinical probability of AL.

关键词： Anastomotic Leakage Esophagectomy multimodal machine learning

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Enhancing 3D Reconstruction Accuracy of FIB Tomography Data Using Multi‑voltage Images and multimodal machine learning

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Nanomanufacturing and Metrology 2024年第1期7卷 48-60页

作者： Trushal Sardhara Alexander Shkurmanov Yong Li Lukas Riedel Shan Shi Christian J.Cyron Roland C.Aydin Martin Ritter Institute for Continuum and Material Mechanics Hamburg University of TechnologyHamburgGermany Electron Microscopy Unit Hamburg University of TechnologyHamburgGermany Institute of Materials Physics and Technology Hamburg University of TechnologyHamburgGermany Institute of Materials Mechanics Helmholtz-Zentrum HereonGeesthachtGermany Research Group of Integrated Metallic Nanomaterials Systems Hamburg University of TechnologyHamburgGermany Institute of Material Systems Modeling Helmholtz-Zentrum HereonGeesthachtGermany

FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of *** approach involves collecting in-plane SEM imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image ***,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current *** artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm *** study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of *** treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.

关键词： multimodal machine learning Multi-voltage images FIB-SEM Overdeterministic systems 3D reconstruction FIB tomography

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A Systematic Literature Review on multimodal machine learning: Applications, Challenges, Gaps and Future Directions

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IEEE ACCESS 2023年 11卷 14804-14831页

作者： Barua, Arnab Ahmed, Mobyen Uddin Begum, Shahina Malardalen Univ Sch Innovat Design & Engn S-72220 Vasteras Sweden

multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role in tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) was applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

关键词： Market research Visualization Systematics Representation learning Medical services Biomedical imaging Bibliographies multimodal machine learning systematic literature review representation translation alignment fusion co-learning

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