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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Chan, Tsz Nam Li, Zhe Leong Hou, U. Cheng, Reynold Shenzhen University Department of Computer Science and Software Engineering China Alibaba Cloud University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China The University of Hong Kong Department of Computer Science and Musketeers Foundation Institute of Data Science Hong Kong

ISBN: (纸本)9798350317152

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to largescale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models. Experimental results verify that this approach can achieve the best trade-off between accuracy, memory space, and training time compared with different types of state-of-the-art methods. © 2024 IEEE.

关键词： Additives

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A Graph-based approach to Mitigate Drug-Drug Interactions and Optimize Therapeutic Regimens 23

A Graph-based approach to Mitigate Drug-Drug Interactions an...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Spanakis, Marios Tzamali, Eleftheria Tzedakis, Georgios Spanakis, Emmanouil G. Tsatsakis, Aristides Sakkalis, Vangelis Department of Forensic Sciences & Toxicology School of Medicine University of Crete Computational Bio-Medicine Laboratory Institute of Computer Science Foundation for Research & Technology - Hellas Heraklion Greece Computational Bio-Medicine Laboratory Institute of Computer Science Foundation for Research & Technology - Hellas Heraklion Greece

ISBN: (纸本)9798350393118

Drug-drug interactions (DDIs) pose a significant issue in modern healthcare, potentially compromising treatment efficacy and patient safety. DDIs arise when significant alterations occur in the pharmacological action of a drug due to its co-administration with another drug, leading to potential adverse drug reactions (ADRs), toxicity or diminished therapeutic efficacy. Apart from the obvious cases of drug combinations that should be avoided, there are instances where risk-benefit analysis may allow co-administration. Hence, DDIs may represent clinically significant cases depending on the clinical outcome, time point of administration, etc. The issue is especially critical in cases of patients with multimorbidity and complex therapeutic regimens with different time points in drug administrations. This work employs a graph-based approach aimed at optimizing therapeutic regiments while considering the minimization of DDIs potential. In this approach each drug is represented as a node, and edges represent the clinical significance of DDIs. We aim to identify sets of drugs that either have no DDIs or exhibit minor to moderate clinical significance (referred to as Maximal Independent Sets), indicating that they can be taken together. In practice, we solved the complementary problem, which is finding Maximal Cliques. Both problems are NP-hard, but for small graphs, they can be solved exactly. From all the cliques we identify, those selected to be a part of each proposed therapeutic regimen must consist of nodes that appear only once. This problem is once again reduced to clique finding. The above approach is demonstrated using two clinical scenarios involving two patients who are experiencing polypharmacy and are at risk for ADRs due to potential DDIs of varying clinical significance. By applying our approach, the therapeutic schemes are optimized towards minimizing the risk of ADRs. © 2023 IEEE.

关键词： Toxicity

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Machine Learning for Subgraph Extraction: Methods, Applications and Challenges 49th

Machine Learning for Subgraph Extraction: Methods, Applicati...

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49th International Conference on Very Large Data Bases, VLDB 2023

作者： Yow, Kai Siong Liao, Ningyi Luo, Siqiang Cheng, Reynold School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science Guangdong-Hong Kong-Macau Joint Laboratory HKU Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong

Subgraphs are obtained by extracting a subset of vertices and a subset of edges from the associated original graphs, and many graph properties are known to be inherited by subgraphs. Subgraphs can be applied in many areas such as social networks, recommender systems, biochemistry and fraud discovery. Researchers from various communities have paid a great deal of attention to investigate numerous subgraph problems, by proposing algorithms that mainly extract important structures of a given graph. There are however some limitations that should be addressed, with regard to the efficiency, effectiveness and scalability of these traditional algorithms. As a consequence, machine learning techniques—one of the most latest trends—have recently been employed in the database community to address various subgraph problems considering that they have been shown to be beneficial in dealing with graphrelated problems. We discuss learning-based approaches for four well known subgraph problems in this tutorial, namely subgraph isomorphism, maximum common subgraph, community detection and community search problems. We give a general description of each proposed model, and analyse its design and performance. To allow further investigations on relevant subgraph problems, we suggest some potential future directions in this area. We believe that this work can be used as one of the primary resources, for researchers who intend to develop learning models in solving problems that are closely related to subgraphs. © 2023, VLDB Endowment. All rights reserved.

关键词： Machine learning

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Exploring the Predictive Capabilities of AlphaFold Using Adversarial Protein Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3384-3392页

作者： Alkhouri, Ismail R. Jha, Sumit Beckus, Andre Atia, George Jha, Susmit Ewetz, Rickard Velasquez, Alvaro OrlandoFL32816 United States Michigan State University Computational Mathematics Science and Engineering Department East LansingMI48824 United States University of Michigan Electrical Engineering and Computer Science Department Ann ArborMI48109 United States Florida International University Knights Foundation School of Computing and Information Sciences MiamiFL33199 United States Air Force Research Laboratory Rome Information Directorate NY13441 United States Department Computer Science Department OrlandoFL32816 United States SRI International Computer Science Laboratory Menlo ParkCA94025 United States Electrical and Computer Engineering Department OrlandoFL32816 United States University of Colorado Computer Science Department BoulderCO80309 United States

Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been fully explored. This is particularly relevant given the broad social implications of such technologies and the fact that biologically small perturbations to noncritical residues of a protein sequence do not typically lead to drastic changes in the protein structure. Our study demonstrates that, similar to adversarial methods in machine learning, small changes to protein sequences can result in significant differences in the predicted protein structures using AlphaFold as determined by large distance measures. Despite this, our findings using multiple protein sequences suggest that AlphaFold is able to accurately predict the domain structure and folding regions of a protein. To gauge structural differences, we employ two alignment-based measures [root-mean-square deviation (RMSD) and the global distance test (GDT) similarity], and one alignment-free measure, which is an effective graph-based structure representation (GraSR) method. We prove that the problem of minimally perturbing protein sequences is NP-complete. Based on the well-established block substitution matrices (BLOSUM62) sequence alignment scoring matrix, we generate adversarial sequences. In our experimental evaluation, we consider 111 proteins (including 29 COVID-19 sequences) in the Universal Protein resource (UniProt), a central resource for protein data. Our findings suggest that, despite the high RMSD values returned by AlphaFold, it is capable of handling the BLOSUM adversarial sequences considered in our analysis, as evidenced by the preservation of the folded regions and the GraSR results. © 2020 IEEE.

关键词： Neural networks

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Multimodal Pain Assessment Βased on Physiological Biosignals: The Impact of Demographic Factors on Perception and Sensitivity 11

Multimodal Pain Assessment Βased on Physiological Biosignal...

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11th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Pavlidou, Elisavet Tsiknakis, Manolis Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Heraklion71410 Greece Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research & Technology-Hellas Vassilika Vouton Heraklion70013 Greece

ISBN: (纸本)9789897587436

Pain is a multidimensional and highly personalized sensation that affects individuals’ physical and emotional state. Visual analog scales, numeric rate indicators, and various questionnaires, all relying on patient-reported outcome measurements, are considered the "gold" standard methods for assessing the severity of pain. Nevertheless, self-report tools require cognitive, linguistic, and social abilities, which may manifest variations in certain populations such as neonates, individuals with intellectual disabilities, and those affected by dementia. The purpose of this study is to automate the process through multimodal physiological-data-driven machine-learning models in order to gain deeper insights into pain sensation. We developed a pipeline using electrocardiogram (ECG), galvanic skin response (GSR), and electromyogram (EMG), along with demographic information from the BioVid dataset. The Pan & Tompkins algorithm was applied for ECG signal processing, while statistical analysis was used for feature extraction across all signals. Our study achieved 82.83% accuracy in the SVM classification task of baseline (BL) vs the highest level of pain (PA4) for females aged 20-35. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Sensory perception

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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

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Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing

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SN computer science 2024年第4期5卷 363页

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Amar, Oumkeltoum Chekroun, Mohamed Hasidi, Oussama Lachihab, Oussama Computer Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakech Morocco SEIA Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco R & amp D and Engineering Center Reminex Managem Group Marrakech Morocco Computer and Systems Engineering Laboratory (L2IS) Cadi Ayyad University Marrakech Morocco

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional Long Short-Term Memory (ConvLSTM). Focused on the Zinc flotation circuit at CMG Managem Group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvLSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth behavior through video data, we gain insights into mineral concentration fluctuations, a critical aspect of traditional analysis. By offering a comprehensive solution that combines cutting-edge technology, meticulous data analysis, and practical augmentation methods, we aim to elevate not only mineral processing efficiency but also a broader spectrum of resource utilization and sustainable practices. This study offers a promising solution to elevate mineral processing efficiency and accuracy in the face of data limitations. It underscores the potential for transformative impacts on mineral processing operations, paving the way for more effective resource utilization and sustainable practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Data augmentation Industrial data analysis Machine learning Mineral processing

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Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6659-6671页

作者： Kang, Guosheng Wang, Yang Liu, Jianxun Cao, Buqing Xiao, Yong Xu, Yu Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China University of Cambridge Cambridge Baker Systems Genomics Initiative British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care CambridgeCB1 8RN United Kingdom

With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup-API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabαsi-Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi-Barabαsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation. © 2020 IEEE.

关键词： Web services

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Firecast Zigzag Convolutional Network for Wildfire Prediction

Firecast Zigzag Convolutional Network for Wildfire Predictio...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Yuzhou Castillo, Joel Chacon Lee, Huikyo Gel, Yulia R. University of California Department of Statistics Riverside United States Center for Research in Mathematics Department of Computer Science United States California Institute of Technology Jet Propulsion Laboratory United States Virginia Tech Department of Statistics United States National Science Foundation United States

ISBN: (纸本)9798350362480

Each year wildfires result in billions of dollars in property damage. Being one of the major natural hazards, wildfires nowadays are also a global affair whose negative impact is particularly devastating in developing countries. As wildfires are expected to become more frequent and severe, more accurate models to predict wildfires are vital to mitigating risks and developing more informed decision-making. Artificial intelligence (AI) has a potential to enhance wildfire risk analytics on multiple fronts. For example, deep learning (DL) has been successfully used to classify active fires, burned scars, smoke plumes and to track the spread of active wildfires. Since wildfire spread tends to exhibit highly complex spatio-temporal dependencies which often cannot be accurately described with conventional Euclideanbased approaches, we postulate that the tools of topological and geometric deep learning, specifically designed for non-Euclidean objects such as manifolds and graphs, may offer a more competitive solution. We validate the proposed methodology to predict wildfire occurrences in Greece and several regions of Africa. Our results indicate that the Firecast Zigzag Convolutional Network (F-ZCN) outperforms the current baseline methods for wildfire prediction and opens a path for more accurate wildfire risk analytics, even in scenarios of limited and noisy data records. © 2024 IEEE.

关键词： Premixed flames

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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