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2024 IEEE International Conference on Big Data, BigData 2024

作者： Suganya, R. Sharma, Abhiram Srivats, R. Kalyanasundaram, V. Shanmugam, Selvanayaki Kolandapalayam Vellore Institute of Technology School of Computer Science and Engineering Chennai India Ashland University Department of the Mathematics and Computer Science AshlandOH United States

ISBN: (纸本)9798350362480

This work portrays developing and evaluating deep learning models for automatically identifying retinal injuries using Optical Coherence Tomography (OCT) images. In particular, this study optimizes three pre-trained Convolutional Neural Networks (CNNs), VGG16, EfficientNetB3, and ResNet50, using transfer learning to classify retinal images into four categories, Drusen, Normal, Diabetic Macular Edema (DME), and Choroidal Neovascularization (CNV). In addition to high-performance classification, the study integrates Explainable Artificial Intelligence (XAI) methods, utilizing Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the retinal regions influencing the model's predictions visually. This offers critical interpretability for clinical settings. The Key performance metrics indicators include accuracy, precision, and F1-score and help to evaluate the models. With the available selected models, EfficientNetB3 achieved the highest performance with an accuracy of 93%, outperforming VGG16 and ResNet50. The application of Grad-CAM highlights the increase in transparency and prediction trust of this model's decision-making process. This study demonstrated the profound learning model efficacy, particularly EfficientNetB3, for accurate retinal injury detection and provides valuable insights into model interpretability for clinical use. © 2024 IEEE.

关键词： Convolutional neural networks

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The effects of gender and URM status on the engineering professional identity of upper-year engineering students 129

The effects of gender and URM status on the engineering prof...

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129th ASEE Annual Conference and Exposition: Excellence Through Diversity, ASEE 2022

作者： Atwood, Sara A. Gilmartin, Shannon Katherine Sheppard, Sheri School of Engineering Mathematics and Computer Science

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Low-complexity signal detection for massive MIMO systems via trace iterative method

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Journal of Systems engineering and Electronics 2024年第3期35卷 549-557页

作者： IMRAN A.Khoso ZHANG Xiaofei ABDUL Hayee Shaikh IHSAN A.Khoso ZAHEER Ahmed Dayo College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China School of Electrical Engineering Korea UniversitySeoul 02841Korea School of Mathematics South China University of TechnologyGuangzhou 510641China Department of Computer Science Huanggang Normal UniversityHuanggang 438000China

Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high *** avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is *** combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection ***,the relationship between implicit and explicit techniques is ***,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink *** proposed improved Newton iteration significantly reduces the complexity of conventional Newton ***,its complexity is still high for higher ***,it is applied only for first two *** subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton *** guarantees of the proposed detector are also *** simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

关键词： signal detection low-complexity linear minimum mean square error(MMSE) massive multiple-input multiple-output(MIMO) trace iterative method(TIM)

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The neutron transport equation in exact differential form

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science China(Physics,Mechanics & Astronomy) 2025年第7期Mechanics & Astronomy) .卷 87-107页

作者： Dong Liu Yang Liu Haoning Dang Kai Wang Bin Zhang Fei Wang Zhouyu Liu Yong Jiang Nuclear Power Institute of China State Key Laboratory of Advanced Nuclear Energy Technology College of Computer Science (School of Software and School of Artificial Intelligence) Sichuan University Zhejiang Institute of Modern Physics School of PhysicsZhejiang University School of Mathematics and Statistics Xi'an Jiaotong University School of Energy and Power Engineering Xi'an Jiaotong University

Derived from the Boltzmann equation,the neutron transport equation describes the motions and interactions of neutrons with nuclei in nuclear devices such as nuclear *** collision or fission effect are described as integral terms which arrive in an integro-differential neutron transport equation(IDNT).Only for mono-material or simple geometries conditions,elegant approximation can simplify the transport equation to provide analytic *** solve this integro-differential equation becomes a practical engineering *** development of deep-learning techniques provides a new approach to solve them but for some complicated conditions,it is also time *** optimize solving the integro-differential equation particularly under the deep-learning method,we propose to convert the integral terms in the integro-differential neutron transport equation into their corresponding antiderivatives,providing a set of fixed solution constraint conditions for these antiderivatives,thus yielding an exact differential neutron transport equation(EDNT).The paper elucidates the physical meaning of the antiderivatives and analyzes the continuity and computational complexity of the new transport equation *** illustrate the significant advantage of ENDT,numerical validations have been conducted using various numerical methods on typical benchmark *** numerical experiments demonstrate that the EDNT is compatible with various numerical methods,including the finite difference method(FDM),finite volume method(FVM),and *** to the IDNT,the EDNT offers significant efficiency advantages,with reductions in computational time ranging from several times to several orders of *** EDNT approach may also be applicable for other integro-differential transport theories such as radiative energy transport and has potential application in astrophysics or other fields.

关键词： neutron transport equation exact differential form integro-differential equation deep learning antiderivative transformation

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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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science China(Information sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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Performance-Guaranteed Finite-Time Tracking of Strict-Feedback Systems with Unknown Control Directions: A Novel Switching Mechanism

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IEEE Transactions on Automatic Control 2025年第6期70卷 4061-4068页

作者： Mao, Bing Wu, Xiaoqun Fan, Ziye Lu, Jinhu Chen, Guanrong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Wuhan University School of Mathematics and Statistics Hubei430072 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong

The problem of achieving performance-guaranteed finite-time exact tracking for uncertain strict-feedback nonlinear systems with unknown control directions is addressed. A novel logic switching mechanism with monitoring functions is designed to deal with unknown control directions. This approach is different from existing methods like the Nussbaum gain technique and smooth orientation functions, which are inapplicable to finite-time exact tracking. The main challenge lies in designing monitoring functions and determining switching rules, which is well resolved through three steps. Most significantly, in contrast to existing works that solely guarantee bounded tracking or asymptotic exact tracking, the new mechanism incorporates novel integral sliding mode techniques to achieve finite-time exact tracking with guaranteed performance. Finally, simulations are presented with comparison to demonstrate the effectiveness of the proposed methods. © 1963-2012 IEEE.

关键词： Feedback

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On k-Planar Graphs Without Short Cycles 32

On k-Planar Graphs Without Short Cycles

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32nd International Symposium on Graph Drawing and Network Visualization, GD 2024

作者： Bekos, Michael A. Bose, Prosenjit Büngener, Aaron Dujmović, Vida Hoffmann, Michael Kaufmann, Michael Morin, Pat Odak, Saeed Weinberger, Alexandra Department of Mathematics University of Ioannina Greece School of Computer Science Carleton University Ottawa Canada Department of Computer Science University of Tübingen Germany School of Electrical Engineering and Computer Science University of Ottawa Canada Department of Computer Science ETH Zürich Switzerland Faculty of Mathematics and Computer Science Fern Universität in Hagen Germany

ISBN: (纸本)9783959773430

We study the impact of forbidding short cycles to the edge density of k-planar graphs;a k-planar graph is one that can be drawn in the plane with at most k crossings per edge. Specifically, we consider three settings, according to which the forbidden substructures are 3-cycles, 4-cycles or both of them (i.e., girth ≥ 5). For all three settings and all k ∈ {1, 2, 3}, we present lower and upper bounds on the maximum number of edges in any k-planar graph on n vertices. Our bounds are of the form c n, for some explicit constant c that depends on k and on the setting. For general k ≥ 4 our bounds are of the form c √ kn, for some explicit constant c. These results are obtained by leveraging different techniques, such as the discharging method, the recently introduced density formula for non-planar graphs, and new upper bounds for the crossing number of 2- and 3-planar graphs in combination with corresponding lower bounds based on the Crossing Lemma. © Michael A. Bekos, Prosenjit Bose, Aaron Büngener, Vida Dujmović, Michael Hoffmann, Michael Kaufmann, Pat Morin, Saeed Odak, and Alexandra Weinberger.

关键词： Graphic methods

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BMWP: the first Bengali math word problems dataset for operation prediction and solving

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Discover Artificial Intelligence 2025年第1期5卷 1-15页

作者： Mondal, Sanchita Khatua, Debnarayan Mandal, Sourav Prasad, Dilip K. Sekh, Arif Ahmed School of Medical Science and Technology IIT Kharagpur West Bengal Kharagpur721302 India Department of Mathematics and Statistics Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur512213 India School of Computer Science and Engineering XIM University Odisha Puri752050 India Department of Computer Science Uit the Arctic University of Norway Tromsø9017 Norway

Solving math word problems of varying complexities is one of the most challenging and exciting research questions in artificial intelligence (AI), particularly in natural language processing (NLP) and machine learning (ML). Foundational language models such as GPT must be evaluated for intelligence, and solving word problems is a key method for this assessment. These problems become especially difficult when presented in low-resource regional languages such as Bengali. Word problem solving integrates the cognitive domains of language processing, comprehension, and transformation into real-world solutions. During the past decade, advances in AI and machine learning have significantly progressed in addressing this complex issue. Although researchers worldwide have primarily utilized datasets in English and some in Chinese, there has been a lack of standard datasets for low-resource languages such as Bengali. In this pioneering study, we introduce the first Bengali Math Word Problem Benchmark Data Set (BMWP), comprising 8653 word problems. We detail the creation of this dataset and the benchmarking methods employed. Furthermore, we investigate operation prediction from Bengali word problems using state-of-the-art deep learning (DL) techniques. We implemented and compared various standard DL-based neural network architectures, achieving an accuracy of 92±2%. The data set and the code will be available at https://***/SanchitaMondal/BMWP. © The Author(s) 2025.

关键词： Recurrent neural networks

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A Trustworthy Decentralized Federated Learning Framework for Consumer Electronics: Mitigating Large-Scale AIoT Heterogeneity through Transfer Knowledge Integration

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1918-1925页

作者： Chen, Zheyi Ren, Yunjing Xue, Yujie Jolfaei, Alireza Tolba, Amr Yu, Keping Feng, Hailin Zhejiang A and F University College of Mathematics and Computer Science Hangzhou China Flinders University Australia King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan

IoT-enabled consumer electronics can collect and analyze data to improve functionality and user experiences, increasingly becoming part of edge computing networks. Decentralized federated learning is envisioned as a promising AIoT framework to leverage the data generated by these interconnected consumer electronics without compromising user privacy, where computations are performed closer to the data source rather than in a centralized cloud-based data center. However, a number of obstacles bottleneck the deployment of decentralized learning frameworks under a large-scale AIoT scenario, such as heterogeneity issues, communication efficiency, large-scale issues and others. Specifically, heterogeneity issues include diverse learning models, non-i.i.d. data distribution and unbalanced datasets. In this paper, we propose a trustworthy and adaptive knowledge distillation based decentralized federated learning framework to overcome a number of heterogeneity and large-scale issues. To achieve this goal, we formulate a decentralized optimization problem which formally presents various heterogeneity issues. The proposed framework is composed of two components, including a knowledge distillation based algorithm to transfer knowledge over diverse learning models as well as a real-time evaluation module to adapt client drift issues. Convergence analysis demonstrates that the convergence of the proposed framework is guaranteed. Finally, we conduct extensive experiments over three public datasets to validate the effectiveness and efficiency of proposed framework in the large-scale IoT scenario, illustrating that the proposed framework could significantly improve the learning performance over various metrics and outperforming state-of-the-art baseline approaches. © 1975-2011 IEEE.

关键词： Federated learning

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Wide and fault diameter in Kneser graphs for enhanced fault tolerance in parallel computing

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International Journal of Networking and Virtual Organisations 2024年第3期31卷 169-190页

作者： Sundara Rajan, R. Kirithiga Nandini, G. Lin, Yuqing Reji, Remi Mariam Department of Mathematics Hindustan Institute of Technology and Science Chennai603 103 India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai603 103 India School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia Department of Mathematics Christian College Kerala Chengannur689 122 India

A system’s fault tolerance is its capacity to function even if one or more of its components fail. Implementing a fault-tolerant network becomes an important criterion for reliable computing. Reliability measures play a significant part in recognising the role of faulty and non-faulty processors in a parallel computing system. Parallel computing is used primarily for saving time, solving big problems, and doing multiple tasks at once at the same time. Various reliability measures have been introduced to evaluate a network’s fault-tolerance capability. We have measured the wide diameter and fault diameter of the Kneser graphs in this study. Also, we have verified the fault diameter obtained using an experimental study. Further, we have described some applications of wide diameter and fault diameter in parallel and distributed computing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Fault tolerant computer systems

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