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Retraction Note: Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction

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Neural Computing and Applications 2024年第1期37卷 513-513页

作者： El-Rashidy, Nora ElSayed, Nesma E. El-Ghamry, Amir Talaat, Fatma M. Machine Learning and Information Retrieval Department Faculty of Artificial Intelligence Kafrelsheiksh University Kafrelsheiksh Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta Egypt School of Engineering and Computer Science University of Hertfordshire Egypt Hosted By Global Academic Foundation Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University New Mansoura Egypt

Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design

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arXiv 2023年

作者： Karandashev, Konstantin Weinreich, Jan Heinen, Stefan Arrieta, Daniel Jose Arismendi von Rudorff, Guido Falk Hermansson, Kersti von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry-Angström Laboratory Uppsala University Box 538 UppsalaSE-75121 Sweden University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Strase 40 Kassel34132 Germany Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science, but also a very difficult one due to the vast number of possible molecular systems. We propose an Evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favourable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap;optimization was done over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) datasets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 106 QM calculations and were thus only feasible thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Electrolytes

DRG-Net: Interactive Joint learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading

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arXiv 2022年

作者： Tusfiqur, Hasan Md Nguyen, Duy M.H. Truong, Mai T.N. Nguyen, Triet A. Nguyen, Binh T. Barz, Michael Profitlich, Hans-Jürgen Than, Ngoc T.T. Le, Ngan Xie, Pengtao Sonntag, Daniel Germany Machine Learning and Simulation Lab University of Stuttgart Germany Department of Multimedia Engineering Dongguk University Korea Republic of Department of Mathematics University of Architecture Ho Chi Minh City Viet Nam AISIA Lab University of Science VNUHCM Viet Nam Byers Eye Institute Stanford University United States Department of Computer Science and Computer Engineering University of Arkansas United States Department of Electrical and Computer Engineering University of California San Diego United States Applied Artificial Intelligence Oldenburg University Germany

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas and provide visual explanation and (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system’s predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner. © 2022, CC BY.

关键词： Grading

Advanced sound-based pest detection in agriculture using deep learning and adaptive optimization

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： R, Sahana Lokesh SailajaKumar, K. S.Soundariya, R. K S, Kavitha Reshma, S. Department of Computer Science & Engineering (Cyber Security) Ramaiah Institute of Technology Bengaluru India SoCSE RV University Bangalore India Department of Computer Technology Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Artificial Intelligence and Machine Learning Dayananda Sagar College of Engineering Bengaluru India

Implementing sound-based pest detection systems in large fields comes with challenges, including the complexity of acoustic data, interference from environmental noise, and the need for scalable, real-time solutions capable of processing data over vast areas. To address these challenges, we propose an innovative pest detection system that integrates advanced sound analytics with deep learning techniques. This study proposes a feature selection technique that includes the real-valued discrete Gabor transform algorithm and linear frequency Cepstrum coefficients (LFCC) algorithm, which helps analyze the frequency spectrum and capture critical features. The approach operates in four stages: pre-processing, feature selection, feature matrix generation and sound classification. This study proposes primarily, that data is collected and pre-processed, followed by the feature selection technique including the Real-Valued Discrete Gabor Transform algorithm and Linear Frequency Cepstrum Coefficients (LFCC) algorithm, which help analyze the frequency spectrum and capture critical features. These selected features are then generated as a feature matrix. After that, the pest sound is classified using Modified EfficientNet. To optimize the model’s parameters and enhance its performance and accuracy, the Hippopotamus Optimization Algorithm (HOA) is integrated with EfficientNet. The dataset was utilized to validate the suggested approach, with accuracy levels of over 98% and specificity is 97%, respectively. These results demonstrate superior classification efficiency compared to existing pest detection systems. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Feature matrix Hippopotamus Optimization Algorithm Linear Frequency Cepstrum Coefficients algorithm Modified EfficientNet Real-Valued Discrete Gabor Transform algorithm

Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging 21

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Efficient hierarchical Bayesian inference for spatio-tempora...

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ali Hashemi Yijing Gao Chang Cai Sanjay Ghosh Klaus-Robert Müller Srikantan S. Nagarajan Stefan Haufe Uncertainty Inverse Modeling and Machine Learning Group Technische Universität Berlin Germany and Machine Learning Group Technische Universität Berlin Germany Department of Radiology and Biomedical Imaging University of California San Francisco Department of Radiology and Biomedical Imaging University of California San Francisco and National Engineering Research Center for E-Learning Central China Normal University China Machine Learning Group Technische Universität Berlin Germany and BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany and Department of Artificial Intelligence Korea University South Korea and Max Planck Institute for Informatics Saarbrücken Germany Uncertainty Inverse Modeling and Machine Learning Group Technische Universität Berlin Germany and Physikalisch-Technische Bundesanstalt Berlin Germany and Charité – Universitätsmedizin Berlin Germany and Bernstein Center for Computational Neuroscience Berlin Germany

ISBN: (纸本)9781713845393

Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models. Examples include M/EEG inverse problems, neural encoding models for task-based fMRI analyses, and climate science. In these domains, both the model parameters to be inferred and the measurement noise may exhibit a complex spatio-temporal structure. Existing work either neglects the temporal structure or leads to computationally demanding inference schemes. Overcoming these limitations, we devise a novel flexible hierarchical Bayesian framework within which the spatio-temporal dynamics of model parameters and noise are modeled to have Kronecker product covariance structure. Inference in our framework is based on majorization-minimization optimization and has guaranteed convergence properties. Our highly efficient algorithms exploit the intrinsic Riemannian geometry of temporal autocovariance matrices. For stationary dynamics described by Toeplitz matrices, the theory of circulant embeddings is employed. We prove convex bounding properties and derive update rules of the resulting algorithms. On both synthetic and real neural data from M/EEG, we demonstrate that our methods lead to improved performance.

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A Class-wise Non-salient Region Generalized Framework for Video Semantic Segmentation

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arXiv 2022年

作者： Zhang, Yuhang Tian, Shishun Liao, Muxin Zhang, Zhengyu Zou, Wenbin Xu, Chen Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Univ Rennes INSA Rennes CNRS IETR - UMR 6164 RennesF-35000 France Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Advanced Machine Learning and Applications Institute of Artificial Intelligence and Advanced Communication College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China

Video semantic segmentation (VSS) is beneficial for dealing with dynamic scenes due to the continuous property of the real-world environment. On the one hand, some methods alleviate the predicted inconsistent problem between continuous frames. On the other hand, other methods employ the previous frame as the prior information to assist in segmenting the current frame. Although the previous methods achieve superior performances on the independent and identically distributed (i.i.d) data, they can not generalize well on other unseen domains. Thus, we explore a new task, the video generalizable semantic segmentation (VGSS) task that considers both continuous frames and domain generalization. In this paper, we propose a class-wise non-salient region generalized (CNSG) framework for the VGSS task. Concretely, we first define the class-wise non-salient feature, which describes features of the class-wise non-salient region that carry more generalizable information. Then, we propose a class-wise non-salient feature reasoning strategy to select and enhance the most generalized channels adaptively. Finally, we propose an inter-frame non-salient centroid alignment loss to alleviate the predicted inconsistent problem in the VGSS task. We also extend our video-based framework to the image-based generalizable semantic segmentation (IGSS) task. Experiments demonstrate that our CNSG framework yields significant improvement in the VGSS and IGSS tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

FedDAG: Federated DAG Structure learning

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arXiv 2021年

作者： Gao, Erdun Chen, Junjia Shen, Li Liu, Tongliang Gong, Mingming Bondell, Howard School of Mathematics and Statistics The University of Melbourne Australia Faculty of Electronic and Information Engineering Xi'an Jiaotong University China JD Explore Academy China TML Lab Sydney AI Centre The University of Sydney Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

To date, most directed acyclic graphs (DAGs) structure learning approaches require data to be stored in a central server. However, due to the consideration of privacy protection, data owners gradually refuse to share their personalized raw data to avoid private information leakage, making this task more troublesome by cutting off the first step. Thus, a puzzle arises: how do we discover the underlying DAG structure from decentralized data? In this paper, focusing on the additive noise models (ANMs) assumption of data generation, we take the first step in developing a gradient-based learning framework named FedDAG, which can learn the DAG structure without directly touching the local data and also can naturally handle the data heterogeneity. Our method benefits from a two-level structure of each local model. The first level structure learns the edges and directions of the graph and communicates with the server to get the model information from other clients during the learning procedure, while the second level structure approximates the mechanisms among variables and personally updates on its own data to accommodate the data heterogeneity. Moreover, FedDAG formulates the overall learning task as a continuous optimization problem by taking advantage of an equality acyclicity constraint, which can be solved by gradient descent methods to boost the searching efficiency. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Directed graphs

Physico-chemical properties extraction from the fluorescence spectrum with 1D-convolutional neural networks: application to olive oil

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arXiv 2022年

作者： Venturini, Francesca Sperti, Michela Michelucci, Umberto Gucciardi, Arnaud Martos, Vanessa M. Deriu, Marco A. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Technikumstrasse 9 Winterthur8401 Switzerland PolitoBIOMed Lab Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy TOELT llc Machine Learning Research and Development Birchlenstr. 25 Dübendorf8600 Switzerland Artificial Intelligence Laboratory University of Ljubljana Ljubljana Slovenia Department of Plant Physiology Faculty of Sciences Biotechnology Institute University of Granada Campus Fuentenueva s/n 18071 Spain

One of the main challenge for olive oil producers is the ability to asses and control oil quality regularly during the production cycle. For this purpose, several parameters need to be determined, such as the acidity, UV absorption coefficients and the ethyl esters content. To achieve this, samples must be sent to an approved laboratory for chemical analysis. This approach is expensive, required costly equipment and scientific training and thus cannot be performed very frequently, making regular and low-cost quality control of olive oil a hard to solve challenge. This work describes a new approach based on fluorescence spectroscopy and artificial intelligence (namely, 1-D convolutional neural networks) to predict the five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters K270and K232, and ethyl esters) from one single fluorescence spectrum obtained with a very fast (less than one second) measurement from a low-cost fluorescence sensor. The measurement does not require any sample preparation or scientific training and can be easily performed even by completely untrained workers. The results indicate that the proposed approach gives exceptional results for quality determination through extraction of the relevant physico-chemical parameters. This would make the continuous quality control of olive oil during and after the entire production cycle a reality, even for small producers. © 2022, CC BY-NC-ND.

关键词： Convolution

Joint Parking and Power Management for Electric Vehicle Edge Computing: A Bilevel Optimization Approach

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Joint Parking and Power Management for Electric Vehicle Edge...

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Xumin Huang Weifeng Zhong Jiangtian Nie Jiawen Kang Zehui Xiong Yuan Wu Mohsen Guizani School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China School of Computer Science and Engineering Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Computer and Information Science University of Macau Macau China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

With the vehicle-to-grid and computing capabilities, a parked electric vehicle (EV) has a dual role, namely being an energy prosumer as well as a computing node for accommodating computation-offloading services. This dual-role feature of EVs yields a new computing paradigm named Electric Vehicle Edge Computing (EVEC). To ease the implementation of EVEC, we propose a fine-grained EV management approach to jointly provide parking guidance for EVs and control their charging/discharging power in parking lots. We formulate a bilevel optimization problem where the top-level problem optimizes the matching between EVs and parking lots from the perspective of computation offloading, and the bottom-level problem optimizes the control of EV charging/discharging power from the view of power networks. We transform the bilevel optimization problem into a single-level form, which is a nonconvex mixed-integer nonlinear programming problem, and we further tackle it by linearization techniques. Finally, we provide numerical results to demonstrate the efficiency and effectiveness of our approach.

关键词： Wireless communication Vehicle-to-grid Systematics Power system management Computational modeling Transforms Relaxation methods