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arXiv 2025年

作者： Patel, Divya Parikh, Vansh Patel, Om Shah, Agam Chaudhury, Bhaskar Group in Computational Science and HPC Dhirubhai Ambani Institute of Information and Communication Technology Gandhinagar India School of Computational Science & Engineering College of Computing Georgia Institute of Technology AtlantaGA United States

In this work, we apply topic modeling using Non-Negative Matrix Factorization (NMF) on the COVID-19 Open Research Dataset (CORD-19) to uncover the underlying thematic structure and its evolution within the extensive body of COVID-19 research literature. NMF factorizes the document-term matrix into two non-negative matrices, effectively representing the topics and their distribution across the documents. This helps us see how strongly documents relate to topics and how topics relate to words. We describe the complete methodology which involves a series of rigorous pre-processing steps to standardize the available text data while preserving the context of phrases, and subsequently feature extraction using the term frequency-inverse document frequency (tf-idf), which assigns weights to words based on their frequency and rarity in the dataset. To ensure the robustness of our topic model, we conduct a stability analysis. This process assesses the stability scores of the NMF topic model for different numbers of topics, enabling us to select the optimal number of topics for our analysis. Through our analysis, we track the evolution of topics over time within the CORD-19 dataset. Our findings contribute to the understanding of the knowledge structure of the COVID-19 research landscape, providing a valuable resource for future research in this field. © 2025, CC BY.

关键词： Non-negative matrix factorization

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Tunable Fano and Dicke effects in quantum transport in double quantum dots sandwiched between topological insulators

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Physical Review B 2025年第15期111卷 155423-155423页

作者： Yuan Hong Zhen-Guo Fu Zhou-Wei-Yu Chen Feng Chi Zhigang Wang Wei Zhang Ping Zhang Institute of Applied Physics and Computational Mathematics and National Key Laboratory of Computational Physics Beijing 100088 China Graduate School China Academy of Engineering Physics Beijing 100088 China School of Mathematics and Computational Science Xiangtan University Xiangtan 411105 China School of Electronic and Information Engineering Zhongshan Institute University of Electronic Science and Technology of China Zhongshan 528400 China

We study the quantum transport in double quantum dots (DQD) sandwiched between surfaces of topological insulator (TI) Bi2Te3, which possess strong spin-orbit coupling (SOC) and dC3v double group symmetry. Different from the spin-conserved case with two-dimensional electron gas (2DEG) electrodes, the conductance displays a universal scaling relation for different Fermi energy associated with the topological nature/linear dispersion of topological surface states. The interplay between direct interdot tunneling and surface state mediated interaction leads to tunable Dicke and Fano effects with changing the interdot distance. We propose nano-rulers with different measurement range and resolution based on the Fano q-factor. Furthermore, when applying an in-plane Zeeman field, a crossover from a double-peak shape to a quad-peak shape in conductance curve appears. Moreover, the rotational symmetry of the system could also be revealed from the conductance pattern. Our findings contribute to a better understanding of the quantum transport in the presence of electrode's SOC topological states.

关键词： Surface states

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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Classification of benign and malignant breast lesions in mammograms using dense-unified multiscale attention network and data-efficient image transformers

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The European Physical Journal Special Topics 2025年 1-19页

作者： Paavankumar, S. Karthik, R. Idayachandiran, Gangatharan Sri, Pulaparthi Penchala Deepthi Illakiya, T. School of Computer Science Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai India

Breast cancer is a common and a serious health problem and it is the major cause of morbidity and mortality for women. Early detection of the disease is particularly challenging because abnormalities such as masses and microcalcifications exhibit subtle and diverse characteristics that are often difficult to identify in mammograms. In recent years, advancement in artificial intelligence, particularly deep learning (DL), has shown to improve diagnostic accuracy and early-stage tumor detection. This study aims to improve performance of DL models by considering both masses and microcalcifications in the proposed work to classify breast cancer abnormalities. The proposed work introduces a novel dual-track network that employs a combination of dense-unified multiscale attention fusion (UMAF) track and data-efficient image transformer (DeiT). The DeiT track processes the entire image simultaneously using patch embeddings, enabling them to capture multiscale representations and dependencies across the entire image. Simultaneously, the Dense-UMAF track focuses on extracting localized features while utilizing connectivity of DenseNet architecture to enable effective feature reuse. This approach generates relevant input features through residual connections of varying lengths, thereby effectively addressing the vanishing gradient problem. The UMAF improves feature extraction by capturing multiscale information, resulting in a better representation of the input data. This dual-track architecture is specifically designed to capture the characteristics of mass and calcification abnormalities in mammograms, which display both localized features and global contextual patterns. The proposed network was evaluated on the Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset, obtaining a classification accuracy of 88.69%.

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Hermitian -(anti-)reflexive solutions to a quaternion matrix equation

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computational and Applied mathematics 2025年第6期44卷 1-14页

作者： Liu, Xin Li, Wanqi Yuan, Shifang Zhang, Yang Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology TaiPa China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology TaiPa China School of Mathematics and Computational Science Wuyi University Jiangmen China Department of Mathematics University of Manitoba Winnipeg Canada

In this paper, we consider the Hermitian $$\{P, k + 1\}$$ -(anti-)reflexive solutions to the quaternion matrix equation $$AXB+CXD=E$$ and $$AX=E,$$ respectively. We use the complex representation method to obtain the necessary and sufficient conditions for the existence of the Hermitian $$\{P, k +1\}$$ -reflexive solution (resp. Hermitian $$\{P, k +1\}$$ -anti-reflexive solution), respectively, and derive their solutions when the matrix equations have the Hermitian $$\{P, k +1\}$$ -reflexive solution (resp. Hermitian $$\{P, k +1\}$$ -anti-reflexive solution). Finally, two examples are provided to verify the effectiveness of our method.

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An Adaptive Proximal Inexact Gradient Framework and Its Application to Per-Antenna Constrained Joint Beamforming and Compression Design

arXiv

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arXiv 2025年

作者： Fan, Xilai Jiang, Bo Liu, Ya-Feng State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Ministry of Education Key Laboratory of NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing210023 China

In this paper, we propose an adaptive proximal inexact gradient (APIG) framework for solving a class of nonsmooth composite optimization problems involving function and gradient errors. Unlike existing inexact proximal gradient methods, the proposed framework introduces a new line search condition that jointly adapts to function and gradient errors, enabling adaptive stepsize selection while maintaining theoretical guarantees. Specifically, we prove that the proposed framework achieves an ϵ-stationary point within O(ϵ−2) iterations for nonconvex objectives and an ϵ-optimal solution within O(ϵ−1) iterations for convex cases, matching the best-known complexity in this context. We then custom-apply the APIG framework to an important signal processing problem: the joint beamforming and compression problem (JBCP) with per-antenna power constraints (PAPCs) in cooperative cellular networks. This customized application requires careful exploitation of the problem’s special structure such as the tightness of the semidefinite relaxation (SDR) and the differentiability of the dual. Numerical experiments demonstrate the superior performance of our custom-application over state-of-the-art benchmarks for the JBCP. © 2025, CC BY.

关键词： Beamforming

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Fuzzy-Based Optimization Techniques for Segmenting the Tumors in Multimodal MRI Images

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Operations Research Forum 2025年第1期6卷 1-27页

作者： Alagarsamy, Saravanan Nagarajan, D. Govindaraj, Vishnuvardhan Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Rajiv Gandhi Salai (OMR) Tamilnadu Chennai India Department of Mathematics Rajalakshmi Institute of Technology Chennai India Division of Data Science of School of Computing Science and Education VIT Bhopal University Kothrikalan Madhya Pradesh Sehore 466114 India

Accurate brain tumor prediction is essential when approaching the field of health care;wherever accuracy in decision-making is crucial, the issues also need to be resolved right away. Many artificial intelligence and machine learning-based techniques have been developed in the last couple of years in the field of healthcare. The intention of this work is to create an algorithm that combines the features of Crow Search (CS) and Interval Type-II Fuzzy Logic System (IT2FLS) algorithms to distinguish the area of tumor from complex brain tissues. The ability of oncologists to make decisions is critical for any therapy sequence to be successful, and the methodology proposed in the research work considerably influences a conclusion through technology interruption. The suggested approach is flexible enough to work with a diversity of image sequences found in the BRATS Challenge 2020 dataset that presents varying degrees of obstacles, challenges, and difficulties in locating the anomalous regions, and it produces improved demarcation results that have been instinctively evaluated and supported by Dice score (98 ± 1.3), specificity (98 ± 1.4), and sensitivity (98 ± 1.7) as average metrics. The motive of this study is the enhancement of the visual perception of oncologists, which gives them improved insight into and comprehension of the condition of the patient. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Crow Search (CS) Interval Type-II Fuzzy Logic System (IT2FLS) Magnetic Resonance Imaging (MRI) Tumor segmentation

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Investigation of Security Attacks in IoMT Devices and Federated Learning as a Mitigation Strategy

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Procedia Computer science 2025年 258卷 3426-3435页

作者： Kiruthika M T Poongodi Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu 603203 India Professor Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bangalore Karnataka India

Internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio health, blood glucose level, blood pressure, etc. The data generated by these devices are very voluminous, and there is an increasing need for safeguarding these sensitive data in order to protect them from malicious attacks. This paper suggests to incorporate Federated learning model to IoMT as a mitigative measure. It is apt to induct FL into IoMT as it operates on a collaborative environment which do not allow transfer of the raw data. Patients’ data are stored at the client end itself and only model updates are shared to the server. In this way, the data and network attacks can be reduced to a great extent. This work proposes an integrated architecture where the layers of FL and IoMT can be knotted up for ensuring privacy and security. This proposed architecture model, further elaborates a distributed working environment. This paper illustrates how FL helps in securing the data produced by the different IoMT devices through the proposed architecture. The challenges involved in the deployment of FL in the framework of IoMT along with potential research directions are discussed further in this research.

关键词： : Federated Learning IoMT Security Internet of Medical Things IoMT Data Attacks Optimization

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Adaptive Agriculture System with Soil Health Prediction using Ensemble Techniques and Smart Irrigation using Deep Learning

Adaptive Agriculture System with Soil Health Prediction usin...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Vasan J K Rithvidas R Hariprasad S M Maria Dominic Savio R S Haritha Sudeshna Shettygari Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Agriculture is a cornerstone of the country’s economy, serving as a fundamental necessity for human survival and a critical driver of socio-economic stability. In traditional farming, the efficiency of crop yield is often limited, and resources are not managed effectively, leading to a significant reduction in overall productivity. This paper introduces an IoT-based adaptive agriculture system using a real-time soil health prediction algorithm, where it leverages the Custom Stacked Ensemble model combined with K-Fold Stratified cross-validation to generate predictions from dynamic environmental and soil conditions. The proposed model achieves an accuracy of 94.5%. Additionally, the system incorporates a smart irrigation mechanism that regulates water delivery to crops, ensuring optimal growth while conserving water resources. The irrigation process is automated using the Enhanced GRU model, which achieves an accuracy of 99.43%, further enhancing efficiency and sustainability. The system collects the data from the sensor in the field via a LoRa-based network that communicates with each other to transmit the data to the IoT server. By combining the weather forecasts and the data obtained from the sensors, the system gives soil suitability for the farmer and also predicts the precise irrigation levels and soil fertility, aiding adaptive farming.

关键词： Irrigation Adaptation models Adaptive systems Accuracy Weather forecasting Soil Prediction algorithms Inference algorithms Water resources Farming

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Conservative mixed Discontinuous Galerkin finite element method with full decoupling strategy for incompressible MHD problems

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Communications in Nonlinear science and Numerical Simulation 2025年 147卷

作者： Huang, Huayi Huang, Yunqing Tang, Qili Yin, Lina Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China National Center for Applied Mathematics in Hunan Xiangtan University Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan411105 China Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China

In this research, we propose a mixed Discontinuous Galerkin (DG) finite element method (FEM) for the numerical solution of incompressible magnetohydrodynamics (MHD) problems, ensuring the divergence-free condition for the velocity field. Our proposed methodology is characterized by its energy stability. Subsequently, we construct a novel finite element discretization scheme, for which we establish the theoretical framework of energy stability. Furthermore, we incorporate a preconditioning strategy that facilitates the development of an alternative positive definite, fully decoupled linearization approach. A significant advantage of this scheme is its ability to circumvent the complexities associated with solving saddle point problems at each time step. We demonstrate the uniqueness and convergence of the solutions within this fully decoupled, block-structured linear system. This theoretical validation is complemented by a series of numerical experiments that substantiate the effectiveness and accuracy of our proposed method. © 2025 Elsevier B.V.

关键词： Finite element method

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