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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Jabri, Jaber Saleh Salim Al Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system. © 2025 IEEE.

关键词： Nutrition

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Multi-Verse Optimization for Node Localization in Wireless Sensor Networks 3

Multi-Verse Optimization for Node Localization in Wireless S...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Pitchaimanickam, B. Muthuvel, P. Rajasekar, R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Tamilnadu Kattankulathur603203 India Saveetha Engineering College Saveetha Nagar Department of Ece Chennai Thandalam602117 India School of Computing Kalasalingam Academy of Research and Education Department of Cse Tamilnadu Krishnankoil India

ISBN: (纸本)9798350369083

Wireless Sensor Networks (WSN) are focused on increasing attention to develop their potential applications in healthcare, agriculture, object tracking, and energy management. Sensor nodes are placed in random locations. The information was gathered from the unknown positions of the sensor nodes, and this information may be useless for the applications. The accurate position of the node is required for more sensitive applications of WSN. This work proposes multi-verse optimization as a method for locating unknown nodes. Particle swarm optimization and firefly algorithms are contrasted with the suggested method. The experimental findings demonstrate that, compared to alternative algorithms, the suggested technique yields superior outcomes. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Collision-Free Linear Time Mutual Visibility for Asynchronous Fat Robots 24

Collision-Free Linear Time Mutual Visibility for Asynchronou...

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25th International Conference on Distributed computing and Networking, ICDCN 2024

作者： Bhagat, Subhash Dey, Paulomi Ray, Rajarshi Department of Mathematics Indian Institute of Technology Jodhpur Jodhpur India St.Thomas' College of Engineering and Technology Kolkata India School of Mathematical & Computational Sciences Indian Association for the Cultivation of Science Kolkata India

ISBN: (纸本)9798400716737

This paper studies the mutual visibility problem for a distributed system of n ≥ 3 asynchronous opaque fat robots under the obstructed visibility model. Robots are represented as unit discs in the Euclidean plane and they block both motions and visions of the other robots through them. The mutual visibility problem requires the robots to reposition themselves in such a way that all the robots in the system become mutually visible to each other. Collision-free movement is one of the desirable features that any algorithm for fat robots needs to satisfy. This paper proposes a distributed collision-free mutual visibility algorithm for asynchronous fat robots with externally visible lights. The proposed algorithm uses O(1) colors for the lights of the robots, assumes one-axis agreement, and runs in O(n) asynchronous rounds. © 2024 ACM.

关键词： Robots

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Benchmarking model chemistry composite calculations for vertical ionization potential of molecular systems

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Chinese Chemical Letters 2025年第2期36卷 508-511页

作者： Bingwei Wang Yihong Ding Xiao Tian Key Laboratory of Carbon Materials of Zhejiang Province Wenzhou Key Lab of Advanced Energy Storage and ConversionZhejiang Province Key Lab of Leather EngineeringCollege of Chemistry and Materials EngineeringWenzhou UniversityWenzhou 325035China School of Mathematics and Science Hebei GEO UniversityShijiazhuang 050031China Laboratory of Theoretical and Computational Chemistry Institute of Theoretical ChemistryJilin UniversityChangchun 130023China

In chemical science,the vertical ionization potential(VIP)is a crucial metric for understanding the electronegativity,hardness and softness of chemical material systems as well as the electronic structure and stability of *** since the last century,the model chemistry composite methods have witnessed tremendous developments in computing the thermodynamic properties as well as the barrier ***,their performance in realm of the vertical electron processes of molecular systems has been rarely *** this study,we for the first time benchmarked the model chemistry composite methods(e.g.,CBS-QB3,G4 and W1BD)in comparison with the commonly used Koopmans's theorem(KT),electron propagator theory(e.g.,OVGF,D2,P3 and P3+)and CCSD(T)methods in calculating the VIP for up to 613 molecular systems with available experimental *** large-scale test calculations strongly showed that the CBS-QB3 model chemistry composite technique can be well recommended to calculate VIP from the perspectives of accuracy,economy and ***,the VIP values of up to 7 molecules were identified to have the absolute errors of larger than 0.3 e V at all calculation levels,which have strong hints that their VIP experimental values should be re-investigated.

关键词： Model chemistry Vertical ionization potential Composite methods Molecular systems CBS-QB3

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αSetup-PCTL:An Adaptive Setup-Based Two-Level Preconditioner for Sequence of Linear Systems of Three-Temperature Energy Equations

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Communications in computational Physics 2022年第10期32卷 1287-1309页

作者： Silu Huang Xiaoqiang Yue Xiaowen Xu Laboratory of Computational Physics Institute of Applied Physics and ComputationalMathematicsBeijing 100088China National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing and Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105China

The iterative solution of the sequence of linear systems arising from threetemperature(3-T)energy equations is an essential component in the numerical simulation of radiative hydrodynamic(RHD)***,due to the complicated application features of the RHD problems,solving 3-T linear systems with classical preconditioned iterative techniques is *** address this difficulty,a physicalvariable based coarsening two-level(PCTL)preconditioner has been proposed by dividing the fully coupled system into four individual easier-to-solve *** its nearly optimal complexity and robustness,the PCTL algorithm suffers from poor efficiency because of the overhead associatedwith the construction of setup phase and the solution of ***,the PCTL algorithm employs a fixed strategy for solving the sequence of 3-T linear systems,which completely ignores the dynamically and slowly changing features of these linear *** address these problems and to efficiently solve the sequence of 3-T linear systems,we propose an adaptive two-level preconditioner based on the PCTL algorithm,referred to as α*** adaptive strategies of the αSetup-PCTL algorithm are inspired by those of αSetup-AMG algorithm,which is an adaptive-setup-based AMG solver for sequence of sparse linear *** proposed αSetup-PCTL algorithm could adaptively employ the appropriate strategies for each linear system,and thus increase the overall *** results demonstrate that,for 36 linear systems,the αSetup-PCTL algorithm achieves an average speedup of 2.2,and a maximum speedup of 4.2 when compared to the PCTL algorithm.

关键词： Sequence of linear systems sparse linear solver preconditioning methods radiation hydrodynamics simulation

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An inexact augmented lagrangian algorithm for training leaky ReLU neural network with group sparsity

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 10097-10139页

作者： Wei Liu Xin Liu Xiaojun Chen Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

The leaky ReLU network with a group sparse regularization term has been widely used in the recent years. However, training such network yields a nonsmooth nonconvex optimization problem and there exists a lack of approaches to compute a stationary point deterministically. In this paper, we first resolve the multi-layer composite term in the original optimization problem by introducing auxiliary variables and additional constraints. We show the new model has a nonempty and bounded solution set and its feasible set satisfies the Mangasarian-Fromovitz constraint qualification. Moreover, we show the relationship between the new model and the original problem. Remarkably, we propose an inexact augmented Lagrangian algorithm for solving the new model, and show the convergence of the algorithm to a KKT point. Numerical experiments demonstrate that our algorithm is more efficient for training sparse leaky ReLU neural networks than some well-known algorithms.

关键词： sparse neural network leaky ReLU group sparsity penalty method inexact augmented Lagrangian method

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Nonequilibrium kinetics effects in Richtmyer–Meshkov instability and reshock processes

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Communications in Theoretical Physics 2023年第11期75卷 99-114页

作者： Yiming Shan Aiguo Xu Lifeng Wang Yudong Zhang National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China National Key Laboratory of Shock Wave and Detonation Physics Institute of Fluid PhysicsChina Academy of Engineering PhysicsMianyang 621900China Center for Applied Physics and Technology MOE Key Center for High Energy Density Physics SimulationsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Mechanics and Safety Engineering Zhengzhou UniversityZhengzhou 450001China

Kinetic effects in the inertial confinement fusion ignition process are far from *** this work,we study the Richtmyer-Meshkov instability and reshock processes by using a two-fluid discrete Boltzmann *** work begins by interpreting the experiment conducted by Collins and Jacobs(2002,*** Mech.464,113-136).It shows that the shock wave causes substances in close proximity to the substance interface to deviate more significantly from their thermodynamic equilibrium *** thermodynamic non-equilibrium(TNE)quantities exhibit complex but inspiring kinetic effects in the shock process and behind the shock *** kinetic effects are detected by two sets of TNE *** first set includes∣Δ_(2)^(*)∣,∣Δ_(3,1)^(*),∣Δ_(3)^(*)∣,and∣Δ_(4,2)^(*)∣,which correspond to the intensities of the non-organized momentum Flux(NOMF),Non-Organized Energy Flux(NOEF),the flux of NOMF and the flux of *** four TNE measures abruptly increase in the shock *** second set of TNE quantities includes■_(NOMF),■_(NOEF)and■_(sum),which denote the entropy production rates due to NOMF,NOEF and their summation,*** mixing zone is the primary contributor to■_(NOEF),while the flow field region outside of the mixing zone is the primary contributor to■_(NOMF).Additionally,each substance exhibits different behaviors in terms of entropy production rate,and the lighter fluid has a higher entropy production rate than the heavier fluid.

关键词： Richtmyer-Meshkov instability discrete Boltzmann method nonequilibrium kinetics effects

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Intrusion Detection System in Mobile Cloud computing Using Bat Optimization Algorithm-Support Vector Machine 2

Intrusion Detection System in Mobile Cloud Computing Using B...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Alabdeli, Haider Yamsani, Nagendar Anitha, D. Chaithra, K.N. Bindu, G. The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350317558

Mobile Cloud computing (MCC) is provided in various industries to acquire cloud-based services by using mobile technology. MCC is employed to minimize mobile device limitations by enabling them to offload computations to remote cloud. It contains significant information about organizations;hence, to avoid this environment from attacks a security solution is required. An Intrusion Detection System (IDS) is a solution to these security problems. The Bat Optimization Algorithm-based Support Vector Machine (BOA-SVM) is developed for IDS by utilizing machine learning in MCC in this research. Firstly, data is acquired by UNSW-NB15 dataset and then min-max normalization is established for normalizing acquired data. GWO is employed for Feature Selection (FS) that determines relevant features for classification. At last, SVM is established to classify IDS effectively and accurately. BOA-SVM attains a better accuracy of 99.78% compared to existing techniques such as Edge-based Hybrid Intrusion Detection Framework (EHIDF), Information Gain-Chi-Square-Particle Swarm Optimization (IG-CS-PSO), and improved Self-adaptive Evolutionary Extreme Learning Machine (SaE-ELM) respectively. © 2024 IEEE.

关键词： Bat Optimization Algorithm Intrusion Detection System Machine Learning Mobile Cloud computing Support Vector Machine

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Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

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Computers, Materials & Continua 2022年第4期71卷 1459-1471页

作者： Anupam Garg Anshu Parashar Dipto Barman Sahil Jain Divya Singhal Mehedi Masud Mohamed Abouhawwash Computer Science and Engineering Department Thapar Institute of Engineering and TechnologyPatialaIndia School of Computer Science and Statistics Trinity College DublinDublinIreland University Institute of Biotechnology Chandigarh UniversityMohaliIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Autism Spectrum Disorder (ASD) is a developmental disorderwhose symptoms become noticeable in early years of the age though it canbe present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completelybut can be reduced if detected early. An early diagnosis is hampered by thevariation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergenceof deep learning approaches in various fields, medical experts can be assistedin early diagnosis of ASD. It is very difficult for a practitioner to identifyand concentrate on the major feature’s leading to the accurate prediction ofthe ASD and this arises the need for having an automated approach. Also,presence of different symptoms of ASD traits amongst toddlers directs tothe creation of a large feature dataset. In this study, we propose a hybridapproach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and preciseprediction of ASD. The proposed framework gives more accurate predictionalong with the recommendations of predicted results which will be a vital aidclinically for better and early prediction of ASD traits amongst toddlers.

关键词： Deep learning explainable artificial intelligence autism spectrum disorder machine learning

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Methodical Tamil Character Recognition Using Fabricated CNN Model

Methodical Tamil Character Recognition Using Fabricated CNN ...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Bhavani, M. Ravikumar, S. Prithi, S. Arockia Raj, Y. Rajendiran, Babu Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Chennai India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350348217

Recognizing text is one of the most challenging problems in computer vision. To put it simply, handwritten character recognition is the process of recognising text in photographs, papers, and other media and converting it into a form that a computer can read. You can solve this issue by employing the image classification method. We will be able to recognise the user's typed Tamil characters in this paper. We designed a brand new model, MVGG16, to handle character recognition and classification. Its basic structure is analogous to that of VGG16, but we added a few more layers to improve accuracy. So far, 156 Tamil characters have been learnt, with 350 images each class. In experimental settings, this model achieves an accuracy of 91.8%. As an input method, this work makes use of a whiteboard where the user can scribble the Tamil character to be recognised. The character will be sent for verification after the user has written it and the image has been exported. Once the model identifies the character, it will show it to the user. © 2023 IEEE.

关键词： Character recognition

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