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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Sathiyaprasad, B. Sreeji, S. Satheeshkumar, B. Menaka, D. Babu, K. Jones, G. Maria Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600 119 India Galgotias University School of Computing Science and Engineering UP Greater Noida India SRM Institute of Science and Technology Katankulathur Department of Computational Intelligence Chennai603203 India

ISBN: (纸本)9798350331790

A large amount of data is collected, analysed, and used on a regular basis by a large number of people for web pages, books, journals, and files which make up a significant portion of the data on the Internet. The burden on significant content-based retrieval and other applications is increasing as the volume of surveillance video data grows. Therefore to reduce the burden during retrieval and to solve the problem of video surveillance an ontology based video retrieval by feature extraction and classification techniques is proposed. In the Initial step the examined form of description files in OWL (Web Ontology Language is fed as input. Then the video pre-processed and segmented before extracting the features. The features of video has been extracted using "Hidden Markov Logic Model With Edge Histogram Descriptor (HMLM-EHD)"in which the spatio based features are extracted with Shape-based features that define object shapes in image is extracted from object contours or regions. After the extraction of features, classification is done using Bayesian network based active learning. The retrieval subsystem evaluates the provided query, performs similarity matching operations (which can be done using the correlation Distance Algorithm), and finally provides the result to the end user for these categorised video frames. The experimental results shows retrieved video frames and their parametric analysis in terms of accuracy of 94%, precision of 85%, recall of 71 % and F-1 score of 73%. © 2023 IEEE.

关键词： Feature extraction

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An Optimization Method for Renewable Energy and Energy Storage Capacity Allocation Based on Temporal Production Simulation 7

An Optimization Method for Renewable Energy and Energy Stora...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Du, Hongbo Li, Chi Chen, Liang Zhang, Jinping Kou, Caixia College of Sciences Beijing University of Posts and Telecommunications Beijing China National Key Laboratory of Renewable Energy Grid-Integration China Electric Power Research Institute Beijing China Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350345094

With the rapid expansion of large-scale renewable energy bases in China, optimizing the allocation of renewable energy and energy storage capacity is crucial for improving system efficiency. In this paper, a joint optimal configuration method of wind-photovoltaic-energy storage capacity based on time series production simulation is proposed for renewable energy transmission base. Its aim is to minimize the total investment cost of the system under certain constraints about the proportion of renewable energy power generation, renewable energy utilization rate, conventional thermal power operation, energy storage power station operation, ultra-high voltage direct current (UHVDC) regulation and power transmission. To handle the nonlinear constraints, linearization is achieved by introducing integer variables. For the complex operation constraints of conventional thermal power and UHVDC channel, a simplified modeling method of start-stop cycle and adjustment cycle is proposed, which can effectively reduce the number of integer variables and improve the solving efficiency. The effectiveness of the proposed method is tested on the data of a renewable energy base in Northwest China. © 2023 IEEE.

关键词： Energy utilization

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A Novel Approach of Disease Diagnostic Prediction Using SMOTE Ensemble Classification 2nd

A Novel Approach of Disease Diagnostic Prediction Using SMOT...

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2nd International Conference on Deep sciences for computing and Communications, IconDeepCom 2023

作者： Gajendran, Sudhakaran Arunarani, A.R. Nair, Akhil R. Logeswari, G. Elakkiya, R. School of Electronics Engineering Vellore Institute of Technology Tamilnadu Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India Department of Computer Science Birla Institute of Technology and Science Pilani Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9783031689079

Hepatitis is a liver inflammation caused primarily by viral infection. There are several methods that help with its diagnosis. However, the available datasets for Hepatitis research contain missing values that are imputed by median values. The Synthetic Minority Oversampling Technique (SMOTE) oversamples the minority class, while feature selection algorithms extract critical features from the actual data. One significant disadvantage of existing approaches is their inability to efficiently deal with missing data, resulting in lower statistical effectiveness. In this paper, a novel approach for predicting disease diagnostic is proposed. Firstly, recursive feature elimination (RFE) is used to remove features from the total number declared, followed by univariate analysis, which provides a score to each feature. The principal component analysis (PCA) compresses data. The mutual information score and ReliefF are two automatic feature selection approaches that restore key features. The synthetic minority oversampling technique (SMOTE) oversamples the minority class to obtain balanced data. Several experiments have been conducted using the hepatitis dataset and the framework has shown to predict the disease much more accurately than existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diseases

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Kinetics of Rayleigh-Taylor instability in van der Waals fluid:the influence of compressibility

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Frontiers of physics 2025年第1期20卷 269-297页

作者： Jie Chen Aiguo Xu Yudong Zhang Dawei Chen Zhihua Chen National Key Laboratory of Transient Physics Nanjing University of Science and TechnologyNanjing 210094China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsP.O.Box 8009-26Beijing 100088China State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of TechnologyBeijing 100081China HEDPS Center for Applied Physics and Technologyand College of EngineeringPeking UniversityBeijing 100871China National Key Laboratory of Shock Wave and Detonation Physics Mianyang 621999China School of Mechanics and Safety Engineering Zhengzhou UniversityZhengzhou 450001China

Early studies on Rayleigh−Taylor instability(RTI)primarily relied on the Navier−Stokes(NS)*** research progresses,it becomes increasingly evident that the kinetic information that the NS model failed to capture is of great value for identifying and even controlling the RTI process;simultaneously,the lack of analysis techniques for complex physical fields results in a significant waste of data *** addition,early RTI studies mainly focused on the incompressible case and the weakly compressible *** the case of strong compressibility,the density of the fluid from the upper layer(originally heavy fluid)may become smaller than that of the surrounding(originally light)fluid,thus invalidating the early method of distinguishing light and heavy fluids based on *** this paper,tracer particles are incorporated into a single-fluid discrete Boltzmann method(DBM)model that considers the van der Waals *** using tracer particles to label the matter-particle sources,a careful study of the matter-mixing and energy-mixing processes of the RTI evolution is realized in the single-fluid *** effects of compressibility on the evolution of RTI are examined mainly through the analysis of bubble and spike velocities,the ratio of area occupied by heavy fluid,and various entropy generation rates of the *** is demonstrated that:(i)compressibility has a suppressive effect on the spike velocity,and this suppressive impact diminishes as the Atwood number(At)*** influence of compressibility on bubble velocity shows a staged behavior with increasing.(ii)The impact of compressibility on the entropy production rate associated with the heat flow(S_(NOEF))is related to the stages of RTI ***,this staged impact of compressibility on S_(NOEF)varies with *** exhibits an inhibitory effect on the entropy production rate associated with viscous stresses(S_(NOEF)).(iii)By incorporating the morphological parameter of the pr

关键词： discrete Boltzmann method Rayleigh-Taylor instability compressibility effect tracer particles

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Enhancing Diabetic Retinopathy Diagnosis with Inception v4: A Deep Learning Approach 5

Enhancing Diabetic Retinopathy Diagnosis with Inception v4: ...

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5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Athilakshmi, R. Jansi, R. Upadhyay, Roshan Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur603203 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350321425

An automated system for the classification of Diabetic Retinopathy (DR) is introduced in this study. DR is a prominent cause of blindness globally, primarily impacting individuals aged 20 to 65. The proposed system can be an essential tool in the sustainable healthcare industry, providing timely and accurate diagnosis and treatment to patients with DR. Current manual screening by ophthalmologists can be time-consuming. Accurate categorization of DR is crucial to provide appropriate care and reduce the likelihood of permanent blindness. The use of big data and deep learning models in DR detection has the potential to revolutionize screening and diagnosis, leading to improved patient outcomes and reduced healthcare costs. This study compares traditional and CNN models, including Decision Trees, Random Forest, KNN, SVM, and Inception V4, to identify the best model for classifying retinal images into five stages of DR severity: No DR, Mild, Moderate, Severe, and Proliferative DR. Among the models tested, Inceptionv4 demonstrated the highest accuracy of 92.83% in classifying DR data. These findings demonstrate the potential of an automated system to improve DR diagnosis and management. © 2023 IEEE.

关键词： Computer vision

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Designing architected materials for mechanical compression via simulation, deep learning, and experimentation

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npj computational Materials 2023年第1期9卷 1534-1542页

作者： Andrew J.Lew Kai Jin Markus J.Buehler Laboratory for Atomistic and Molecular Mechanics(LAMM) Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA 02139USA Department of Chemistry Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA 02139USA Center for Computational Science and Engineering Schwarzman College of ComputingMassachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA 02139USA

Architected materials can achieve enhanced properties compared to their plain *** architecting serves as a powerful design lever to achieve targeted behavior without changing the base ***,the connection between architected structure and resultant properties remains an open field of great interest to many fields,from aerospace to civil to automotive ***,we focus on properties related to mechanical compression,and design hierarchical honeycomb structures to meet specific values of stiffness and compressive *** do so,we employ a combination of techniques in a singular workflow,starting with molecular dynamics simulation of the forward design problem,augmenting with data-driven artificial intelligence models to address the inverse design problem,and verifying the behavior of de novo structures with experimentation of additively manufactured *** thereby demonstrate an approach for architected design that is generalizable to multiple material properties and agnostic to the identity of the base material.

关键词： thereby additive starting

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Securing Neural Network in the Cloud: A Systematic Approach of Cloud Security Based on ANN-SVM Model 4

Securing Neural Network in the Cloud: A Systematic Approach ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arunarani, Ar Vijayagopal, V. Kartheesan, L. Dwivedi, Vijay Kumar Rajesh Kumar, A. Nishant, Neerav School of Computing College of Engineering Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Computer Applications Tamilnadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Surguja India N.S.N. College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Karur India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331540364

Many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor Mutual Authentication and Key Agreement (MAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor MAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVM was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVM. © 2024 IEEE.

关键词： Cloud security

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Precise Analysis of Covariance Identifiability for Activity Detection in Grant-Free Random Access

arXiv

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arXiv 2024年

作者： Luo, Shengsong Ma, Junjie Xu, Chongbin Wang, Xin the Department of Communication Science and Engineering Fudan University Shanghai200433 China The 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

We consider the identifiability issue of maximum-likelihood based activity detection in massive MIMO-based grant-free random access. An intriguing observation by Chen et al. [1] indicates that the identifiability undergoes a phase transition for commonly-used random user signatures as L2, N and K tend to infinity with fixed ratios, where L, N and K denote the user signature length, the total number of users, and the number of active users, respectively. In this letter, we provide a precise analytical characterization of the phase transition based on a spectral universality conjecture. Numerical results demonstrate excellent agreement between our theoretical predictions and the empirical phase transitions. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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Mixed finite element methods for biharmonic obstacle problems

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SeMA Journal 2025年 1-10页

作者： Piersanti, Paolo Sun, Tianyu School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) 2001 Longxiang Blvd. Longgang District Shenzhen China Department of Mathematics and Institute for Scientific Computing and Applied Mathematics Indiana University Bloomington 729 East Third Street Bloomington IN United States

This paper is devoted to the study of an alternative Finite Element Method to the one originally proposed by Ciarlet & Raviart, and then complemented by Ciarlet & Glowinski, for studying the numerical approximation of the solution of the biharmonic problem. In addition to providing an alternative way of approximating the solution of the biharmonic problem, the method here presented is valid also in the context where the solution to the biharmonic problem is subjected to a constraint. The theoretical results we derived are complemented by a series of numerical experiments. © The Author(s), under exclusive licence to Sociedad Española de Matemática Aplicada 2025.

关键词： Elasticity theory Finite element method Obstacle problems Penalty method Variational inequalities

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