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IEEE Symposium on computers and Communications (ISCC)

作者： Nasim Mahmud Nayan Ashraful Islam Muhammad Usama Islam Eshtiak Ahmed Mohammad Mobarak Hossain Md Zahangir Alam Department of Computer Science and Engineering University of Information Technology and Sciences Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Department of Computer Science and Engineering Independent University Bangladesh School of Computing and Informatics University of Louisiana at Lafayette USA Department of Computer Science and Engineering Daffodil International University Bangladesh

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (XAI) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of XAI methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating XAI with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention.

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Quadratic Finite Volume Element Schemes over Triangular Meshes for a Nonlinear Time-Fractional Rayleigh-Stokes Problem

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computer Modeling in engineering & sciences 2021年第5期127卷 487-514页

作者： Yanlong Zhang Yanhui Zhou Jiming Wu Graduate School of China Academy of Engineering Physics Beijing100088China School of Data and Computer Science Sun Yat-Sen UniversityGuangzhou510275China Institute of Applied Physics and Computational Mathematics Beijing100088China

In this article,we study a 2D nonlinear time-fractional Rayleigh-Stokes problem,which has an anomalous subdiffusion term,on triangular meshes by quadratic finite volume element ***-fractional derivative,defined by Caputo fractional derivative,is discretized through L2−1σformula,and a two step scheme is used to approximate the time first-order derivative at time tn−α/2,where the nonlinear term is approximated by using a matching linearized difference scheme.A family of quadratic finite volume element schemes with two parameters are proposed for the spatial discretization,where the range of values for two parameters areβ1∈(0,1/2),β2∈(0,2/3).For testing the precision of numerical algorithms,we calculate some numerical examples which have known exact solution or unknown exact solution by several kinds of quadratic finite volume element schemes,and contrast with the results of an existing quadratic finite element scheme by drawing diversified comparison plots and showing the detailed data of L2 error results and convergence *** results indicate that,L2 error estimate of one scheme with parameters β_(1)=(3−√3)/6,β2=(6+√3−√21+6√3)/9 is O(h^(3)+△t^(2)),and L^(2) error estimates of other schemes are O(h^(2)+△t^(2)),where h and △t denote the spatial and temporal discretization parameters,respectively.

关键词： Quadratic finite volume element schemes anomalous sub-diffusion term L2 error estimate quadratic finite element scheme

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Machine Learning With data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Kirubhakaran D. Karthikeswaran M. Arun R. Arun Chakravarthy J. Akshya M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A* algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry.

关键词： Industries Adaptation models Heuristic algorithms Real-time systems Path planning Vehicle dynamics Collision avoidance Autonomous vehicles Optimization Logistics

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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Kidney Tumor Recognition from Abdominal CT Images using Transfer Learning

Kidney Tumor Recognition from Abdominal CT Images using Tran...

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International Symposium on Multiple-Valued Logic

作者： Sefatul Wasi Saadia Binte Alam Rashedur Rahman M Ashraful Amin Syoji Kobashi Department of Computer Science and Engineering Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh Graduate School of Engineering University of Hyogo Himeji Hyogo Japan

Kidney tumor is a health concern that affects kidney cells and may leads to mortality depending on their type. Benign tumors can be unproblematic whereas malignant tumors pose the threat of kidney cancer. Early detection and diagnosis are possible through kidney tumor recognition based on deep learning techniques. In this paper, a method based on transfer learning using deep convolutional neural network (DCNN) is proposed to recognize kidney tumor from computed tomography (CT) images. The proposed method was evaluated on 5284 images. The final accuracy, precision, recall, specificity and F1 score were 92.54%, 80.45%, 93.02%, 92.38% and 0.8628, respectively.

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Multilevel Particle Filters for Partially Observed McKean-Vlasov Stochastic Differential Equations

arXiv

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arXiv 2024年

作者： Awadelkarim, Elsiddig Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Data Science The Chinese University of Hong Kong Shenzhen China

In this paper we consider the filtering problem associated to partially observed McKean-Vlasov stochastic differential equations (SDEs). The model consists of data that are observed at regular and discrete times and the objective is to compute the conditional expectation of (functionals) of the solutions of the SDE at the current time. This problem, even the ordinary SDE case is challenging and requires numerical approximations. Based upon the ideas in [3, 12] we develop a new particle filter (PF) and multilevel particle filter (MLPF) to approximate the afore-mentioned expectations. We prove under assumptions that, for ϵ > 0, to obtain a mean square error of O(ϵ2) the PF has a cost per-observation time of O(ϵ−5) and the MLPF costs O(ϵ−4) (best case) or O(ϵ−4 log(ϵ)2) (worst case). Our theoretical results are supported by numerical experiments. © 2024, CC BY.

关键词： Mean square error

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Analyzing α-divergence in Gaussian Rate-Distortion-Perception Theory

Analyzing α-divergence in Gaussian Rate-Distortion-Percepti...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Martha V. Sourla Giuseppe Serra Photios A. Stavrou Marios Kountouris School of Electrical & Computer Engineering Technical University of Crete Chania Greece Communication Systems Department EURECOM France Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

The problem of the estimation of the information rate-distortion-perception function (RDPF), which is a relevant information-theoretic quantity in goal-oriented lossy compression and semantic information reconstruction, is investigated here. Specifically, we study the RDPF tradeoff for Gaussian sources subject to a mean-squared error (MSE) distortion and a perception measure that belongs to the family of α-divergences. Assuming a jointly Gaussian RDPF, which forms a convex optimization problem, we characterize an upper bound for which we find a parametric solution. We show that evaluating the optimal parameters of this parametric solution is equivalent to finding the roots of a reduced exponential polynomial of degree α. Additionally, we determine which disjoint sets contain each root, which enables us to evaluate them numerically using the well-known bisection method. Finally, we validate our analytical findings with numerical results and establish connections with existing results.

关键词： Wireless communication Upper bound Semantics Measurement uncertainty Estimation Distortion Polynomials Convex functions Distortion measurement Information theory

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Incorporating 5G NR Sidelink with MQTT to Maximize Energy Efficiency in IoT-enabled Microgrids

Incorporating 5G NR Sidelink with MQTT to Maximize Energy Ef...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： G Neelamegam Hermina JM Ashforn Mohamed S Raja J. Priyadharshini J Akshya Mani Deepak Choudhry M. Sundarrajan Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Department of Computer Science and Engineering Kalaignarkarunanidhi Institute of Technology Coimbatore Department of Information Technology K. Ramakrishnan College of Engineering Trichy Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

IoT for smart microgrids is an innovative disruption at the cutting edge of energy management, relying on interconnectedness provided by smart sensors and devices to enhance power generation and distribution precision in metropolitan areas. The problems of the real-time operational capabilities required for efficient energy resource management thus progress, inhibit rapid control and management. The main goal of this research proposal consists of to use of the advanced technology of 5G NR Sidelink combined with MQTT to conduct a revolutionary communication system of Microgrid running through the IoT devices. The 5G NR (New Radio) Sidelink enables direct D2D communication, which leads to a dramatic lowering of the latency in the absence of traditional network infrastructures for communication, while the MQTT protocol improves the communication efficiency among networks of numerous IoT devices. The introduction of this system was followed by a 15% decrease in energy transmission losses and by 20% improvement in response times in comparison to conventional models specified in the quantitative evaluation thus increasing operational efficiency by far. These efficiencies not only reduce operating expenses and power consumption but also increase the solution's scalability, pointing out the possibility of building the solution for bigger urban microgrid implementations. In consideration of that, the suggested mechanism with 5G NR Sidelink along with MQTT overcomes elaborate barriers of IoT-based microgrids and establishes a new potency in advanced energy management in smart cities which could make the distribution network in the urban areas on the edge of the verge of a change.

关键词： Renewable energy sources 5G mobile communication Scalability Microgrids Sidelink Propagation losses Smart grids

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Generalising Violence Detection with a New Near-Real-World Violence dataset

Generalising Violence Detection with a New Near-Real-World V...

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International Conference on Advancement in Electrical and Electronic engineering (ICAEEE)

作者： Mahmudul Haque Hussain Nyeem Tareque Bashar Ovi Al Nahid Md. Sabbir Hossain Molla Md. Tanjim Mahmud Tuhin Fardin Shahab Ayat Subah Alam Saadia Binte Alam Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department of Electrical Electronic and Communication Engineering (EECE) Military Institute of Science and Technology (MIST) Mirpur Cantonment Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Detecting and classifying violence is crucial for public safety and addressing societal violence. DL models have greatly improved the automation of violence detection systems by effectively capturing intricate visual patterns. However, the quality and diversity of the training data greatly impact the effectiveness of these models. Existing datasets may be biased towards specific situations, limiting their practical use. To address this limitation, we introduce the Movie Clip (MC) dataset to enhance the generalisability of Automated Violence Detection and Classification (AVDC) systems. The MC dataset encompasses a broad spectrum of near-real-world violent actions, incorporating diverse demographics, environmental circumstances, and cultural elements extracted from movies. Consequently, it accurately reflects the complexity and diversity of real-world violent scenarios. The potential of the new dataset is investigated against its existing counterparts, like Hockey Fight (HF) and AIRTLab datasets. These datasets are used to train the ConvLSTM models based on the VGG16 and VGG19 architectures. The proposed dataset significantly improves AVDC model generalisation, outperforming the generalisability of current datasets, thereby advancing violence detection and facilitating the development of more robust and efficient AVDC systems.

关键词： Training Visualization Atmospheric modeling Training data Hafnium Motion pictures Public security

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