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A NEW ADAPTIVE SUBSPACE MINIMIZATION THREE-TERM CONJUGATE GRADIENT ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

Journal of computational mathematics 2021年第2期39卷 159-177页

作者： Keke Zhang Hongwei Liu Zexian Liu School of Mathematics and Statistics Xidian UniversityXi’an 710126China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering computingAMSSChinese Academy of SciencesBeijing 100190China

A new adaptive subspace minimization three-term conjugate gradient algorithm with nonmonotone line search is introduced and analyzed in this *** search directions are computed by minimizing a quadratic approximation of the objective function on special subspaces,and we also proposed an adaptive rule for choosing different searching directions at each *** obtain a significant conclusion that the each choice of the search directions satisfies the sufficient descent *** the used nonmonotone line search,we prove that the new algorithm is globally convergent for general nonlinear functions under some mild *** experiments show that the proposed algorithm is promising for the given test problem set.

关键词： Conjugate gradient method Nonmonotone line search Subspace minimization Sufficient descent condition Global convergence

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Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems 9

Graph Neural Networks for Optimizing Reliability in Vehicle-...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Neelamegam, G. Revathy, J. Kannukkiniyal, M. Devendran, M. Akshya, J. Choudhry, Mani Deepak Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Artificial Intelligence and Data Science Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Hindustan institute of technology Department of Computer Science and Engineering 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)9798350377972

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models;it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data. © 2024 IEEE.

关键词： Graph neural networks

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Stock Price Prediction Using Gradient Boosting Machine with Technical Indicators 3

Stock Price Prediction Using Gradient Boosting Machine with ...

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3rd International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Jayanthi, G. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India Saveetha Nagar Tamil Nadu Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

This research endeavors to design a predictive model to forecast stock prices using a Gradient Boosting Machine (GBM) regressor, specifically emphasizing the adjusted closing price to mitigate the autocorrelation issues intrinsic to time series data. The investigation encompasses a 12-month duration of historical stock data, integrating two technical indicators, namely Exponential Moving Averages (EMA-5 and EMA-10), to augment predictive precision. Gradient Boosting, recognized for its proficiency in managing intricate and non-linear data patterns, was employed to forecast forthcoming stock prices. Critical performance indicators, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2), played a role in evaluating model accuracy, attaining an R2 score of 0.99, thereby signifying an almost perfect correlation between actual and predicted prices. Beyond numerical assessments, visual examinations through scatter plots, residual plots, and prediction error distribution further elucidated the model's efficiency. The findings reveal that the amalgamation of Gradient Boosting with Technical Indicators such as EMA-5 and EMA-10, coupled with an emphasis on the adjusted closing price, proves exceptionally effective for stock price forecasting. This methodology achieved an accuracy rate nearing 99%, illustrating its substantial promise for stock market prediction and its proficiency in alleviating autocorrelation challenges within time series datasets. © 2025 IEEE.

关键词： Autocorrelation

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On the Attractive and Repulsive Forces of Generalized Stochastic Neighbor Embedding With Alpha-Divergence

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IEEE Access 2024年 12卷 90380-90394页

作者： Lin, Hsin-Yi Tseng, Huan-Hsin Chien, Jen-Tzung Seton Hall University Department Of Mathematics And Computer Science South OrangeNJ07079 United States Brookhaven National Laboratory Computational Science Initiative UptonNY11973 United States Institute Of Electrical And Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Stochastic neighbor embedding (SNE) performs nonlinear transformation from high-dimensional observation space to low-dimensional latent space which preserves neighbor affinities. Data pairs in latent space tend to be crowded due to the dimensionality reduction. To mitigate the crowding problem, certain characteristics are favorable in the design of the SNE setting. This study presents a fundamental analysis of SNE that not only generalizes the previous SNEs but also provides a systematic way to understand the intrinsic properties. From the perspective of theoretical connection, we are able to conceive a new generalized SNE (g-SNE) by introducing a regularized power-law distribution with the α-divergence for manifold learning. The proposed method generalizes and incorporates various favorable features for the clustering process. In addition, the proposed method provides high flexibility, admitting tailored realizations to properly reflect the similarity between original and dimension-reduced samples. Experiments are performed to analyze the proposed method, and its effectiveness is demonstrated with several learning tasks. © 2024 The Authors.

关键词： Data visualization

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A Variational Approach to Personalized Federated Learning and Its Improvement 20th

A Variational Approach to Personalized Federated Learning a...

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Wu, Huiwen Zhang, Shuo Research Center for Data Hub and Security Zhejiang Laboratory Hangzhou310000 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789819600250

Federated learning (FL) is a new approach that allows clients from different locations to work together on a global model without sharing raw data. However, training one standard model is not optimal for local optimization due to heterogeneity. Thus, personalized federated learning (PFL) paradigms aim to combine local and global information while preserving diversity among local clients. However, these methods require more theoretical motivation. This paper formalizes the unfired analysis form by defining a linear mapping on the server’s aggregation step for a deeper understanding of the implementation design and to improve existing methods further. Moreover, we show that the existing FL and PFL methods are specific cases in our framework with selected mapping matrices, including FedAvg, FedMTL, FedProx, MAML, and pure local training. Based on that, we propose a novel data-driven graph Laplacian-induced PFL scheme named GLIP-FL to address statistical heterogeneity issues and improve computation efficiency. We implement evaluation experiments of the proposed method on four popular data sets with five FL baselines in three data partitions, both homogeneous and heterogeneous, and the comprehensive experiments show the great potential of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Cloud Based Smart Medical Recommendation System Using Pattern Recognition 1

Cloud Based Smart Medical Recommendation System Using Patter...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Sujatha, E. Shanthasheela, S. Gopirajan, P.V. Department of Computer Science and Engineering Thandalam Chennai602105 India Velammal Institute of Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India

ISBN: (纸本)9798350336009

To build smart medical recommendation system the data needs to be collected from laboratories. The system is intended to provide services as control and manage data samples, industry standards, real test results, reports, human resources in the laboratories, machines, tiny instruments, and automation of the workflow. Combining the data from the laboratories and medical institutions enable ease of communication without time delay. The challenging task is that identifying appropriate hardware and software components which provides compatibility in collecting data and their processing steps, repository storage, secured distribution, and the policies used for information representation in the results. In order to automate the entire process of medical recommendation system the metric parameters are used for determining the efficiency and productivity of the lab to enhance the quality of control systems. The vulnerability lies in maintaining traditional way of records in paper and error in the data. The purpose of this project is to develop the process of daily tasks will be computed through the database using pattern recognition techniques. © 2023 IEEE.

关键词： Recommender systems

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Discovery of Partial Differential Equations from Highly Noisy and Sparse Data with Physics-Informed Information Criterion

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Research 2024年第1期2023卷 247-259页

作者： Hao Xu Junsheng Zeng Dongxiao Zhang BIC-ESAT EREand SKLTCSCollege of EngineeringPeking UniversityBeijing 100871P.R.China Institute of Applied Physics and Computational Mathematics Beijing 100088P.R.China Eastern Institute for Advanced Study Eastern Institute of TechnologyNingbo 315200ZhejiangP.R.China National Center for Applied Mathematics Shenzhen(NCAMS) Southern University of Science and TechnologyShenzhen 518055GuangdongP.R.China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen 518000GuangdongP.R.China

Data-driven discovery of partial differential equations(PDEs)has recently made tremendous progress,and many canonical PDEs have been discovered successfully for proof of ***,determining the most proper PDE without prior references remains challenging in terms of practical *** this work,a physics-informed information criterion(PIC)is proposed to measure the parsimony and precision of the discovered PDE *** proposed PIC achieves satisfactory robustness to highly noisy and sparse data on 7 canonical PDEs from different physical scenes,which confirms its ability to handle difficult *** PIC is also employed to discover unrevealed macroscale governing equations from microscopic simulation data in an actual physical *** results show that the discovered macroscale PDE is precise and parsimonious and satisfies underlying symmetries,which facilitates understanding and simulation of the physical *** proposition of the PIC enables practical applications of PDE discovery in discovering unrevealed governing equations in broader physical scenes.

关键词： process canonical proof

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Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

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International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Rajalakshmi, M. Subramanian, R. S. Sankara Tamilselvan, S. Bhuvanesh, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Satellite image processing often enhances feature perception and visibility for exact feature identification. Additionally, the change improves remote sensing data quality and clarity. Due to their long distances, these photos often have weather-related noise and distortion that must be fixed. This paper addresses these challenges with a Deep Learning (DL) satellite image denoising technique. An Adaptive Genghis Khan Shark Algorithm (AGKSA) and a Convolutional Denoised Autoencoder are used to improve satellite images in this study. The CDAE settings are optimized using the AGKSA while keeping important image attributes. AGKSA is used in this hybrid model to optimize CDAE performance under various noise conditions. Experimental results showed that the proposed model outperforms traditional image denoising approaches in satellite imagery feature extraction and visual quality. In very precise image processing applications like environmental monitoring and land use classification, the CDAE-AGKSA works well. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Adaptive Genghis Khan algorithm Convolutional denoised autoencoder Satellite image enhancement Satellite image processing

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Towards Autonomous Shooting Rover via Situation Aware Visual-Perception and Dynamic Action Execution

Towards Autonomous Shooting Rover via Situation Aware Visual...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Chikoti, Shreenaga Shubham Seelam, Yukkta Jain, Aditya Mehta, Nishi Watsa, Sidhartha Ranjan, Ayush Behera, L. Indian Institute of Technology Kanpur Department of Mathematics and Scientific Computing India Indian Institute of Technology Kanpur Department of Material Science and Engineering India Indian Institute of Technology Kanpur Department of Electrical Engineering India Indian Institute of Technology Kanpur Department of Computer Science and Engineering India Indian Institute of Technology Kanpur Department of Mechanical Engineering India

ISBN: (纸本)9798350312072

In this system paper, we present our approach to the DJI Robomaster AI Challenge, 2022 where our team ERA-IITK was ranked 3rd worldwide among 83 teams. We primarily focus on system hardware design and algorithmic components to achieve full autonomy. We first detail the hardware infrastructure, including intricate details on sensors and communication setup. Then we comprehensively describe our algorithmic pipeline and a state machine based on them to realize an autonomous system. Our major novelty is the high-speed and accurate visual perception module for target detection, and high-speed self-localization to maximize the likelihood of a target being shot. Our research endeavor in this paper can serve as a foundation for more complex systems and holds immense potential for various real-world applications. © 2024 IEEE.

关键词： Vision

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An enhanced three factor based authentication protocol using wireless medical sensor networks for healthcare monitoring

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Journal of Ambient Intelligence and Humanized computing 2024年第1期15卷 1165-1186页

作者： Ali, Rifaqat Pal, Arup Kumar Kumari, Saru Sangaiah, Arun Kumar Li, Xiong Wu, Fan Dhanbad826004 India Department of Mathematics Ch. Charan Singh University Uttar Pradesh Meerut250004 India School of Computing Science and Engineering VIT University Vellore Tamil Nadu632014 India School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Department of Computer Science and Engineering Xiamen Institute of Technology Xiamen361021 China

With the rapid growth of wireless medical sensor networks (WMSNs) based healthcare applications, protecting both the privacy and security from illegitimate users, are major concern issues since patient’s precise information is vital for the proper diagnosis procedure. So, authentication protocol is one of the efficient mechanisms to deal with trustworthy and authentic users. Several authentication protocols have been proposed in WMSNs environment. However, the most of these protocols are so susceptible to security threats and not suitable for practical use. In this article, recently proposed Amin et al.’s authentication scheme is reviewed and some vulnerabilities like off-line password guessing attack, user impersonation attack, known session-key temporary information attack, the revelation of secret parameters, and identity guessing attack are pointed out. To overcome all the above mentioned vulnerabilities, we have proposed an enhanced three-factor based remote user authentication protocol in WMSNs environment. Further, the proposed protocol is validated using Burrows–Abadi–Needham logic and then simulated using Automated Validation of Internet Security Protocols and Applications tool. Moreover, the security analysis ensures that the proposed protocol is well protected from various types of malicious attacks. In addition, the performance evaluation shows better efficiency and suitability of our protocol over other related protocols. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Authentication

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