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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Nelmin, Bathlin N. Shaji, R.S. Hariprasad, S. Shettygari, Sudeshna Vasan, J.K. Vijayan, V. Department of Electronics and Communication Engineering University College of Engineering Nagercoil India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331522667

Approximate computing effectively impacts low-power VLSI circuits by reducing circuit overhead. It introduces minimal error in the output, which does not significantly affect the application. This paper proposes an approximate multiplier constructed using 5_3 and 5_2 counters. The proposed 5_2 counter delivers two outputs instead of three, thereby achieving 60% compression. The overhead of the 5_2 counter circuit is reduced, achieving a power delay product (PDP) improvement of 18%. This reduction comes with an addition of an 6% error rate. The newly designed multiplier has less delay, where the Mean error distance (MED) is kept as low as possible. The complexity of the 5_2 counter is also reduced since the counter uses only a full adder and three gates. This is achieved by applying various error optimization mechanisms. The error parameters, (i) Error Distance (ED), and (ii) Mean Error Distance (MED) are analyzed and compared with existing works. The proposed multipliers are involved in image contrast application with better Peak Signal to Noise Ratio (PSNR) value. © 2025 IEEE.

关键词： Delay circuits

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Friction Dissymmetry on Hexagonal Boron Carbon Nitride

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Nano Letters 2025年第19期25卷 7909-7915页

作者： Lu, Leiling Ying, Tianquan Cui, Chuan-Xin Chen, Yang Leng, Jiantao Li, Jianxin Jiang, Jin-Wu Chang, Tienchong Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Frontier Science Center of Mechanoinformatics School of Mechanics and Engineering Science Shanghai University Shanghai200072 China Department of Engineering Mechanics School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China Joint-Research Center for Computational Materials Zhejiang Laboratory Hangzhou311100 China

The classical friction law posits that macroscopic friction is directly proportional to the normal force, represented by a constant friction coefficient. However, frictional behavior becomes increasingly complex at the nanoscale. We reveal a counterintuitive instance of dissymmetric friction during the sliding of a graphene flake on a hexagonal boron carbon nitride (h-BCN) substrate. This dissymmetry is marked by significantly different friction coefficients in opposing directions along the same sliding path. We attribute this unexpected behavior to the dissymmetric potential energy landscape of h-BCN, which fundamentally differs from the symmetrical profiles seen in graphene and h-BN, despite h-BCN being perceived as a composite of these materials. Our findings enhance the understanding of nanoscale friction and pave the way for the development of innovative nanodevices that utilize directional friction control. © 2025 American Chemical Society.

关键词： Nanocomposites

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An Inexact Bregman Proximal Difference-of-Convex Algorithm with Two Types of Relative Stopping Criteria

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Journal of Scientific computing 2025年第3期103卷 1-42页

作者： Yang, Lei Hu, Jingjing Toh, Kim-Chuan School of Computer Science and Engineering and Guangdong Province Key Laboratory of Computational Science Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Mathematics Institute of Operations Research and Analytics National University of Singapore Singapore Singapore

In this paper, we consider a class of difference-of-convex (DC) optimization problems, which require only a weaker restricted L-smooth adaptable property on the smooth part of the objective function, instead of the standard global Lipschitz gradient continuity assumption. Such problems are prevalent in many contemporary applications such as compressed sensing, statistical regression, and machine learning, and can be solved by a general Bregman proximal DC algorithm (BPDCA). However, the existing BPDCA is developed based on the stringent requirement that the involved subproblems must be solved exactly, which is often impractical and limits the applicability of the BPDCA. To facilitate the practical implementations and wider applications of the BPDCA, we develop an inexact Bregman proximal difference-of-convex algorithm (iBPDCA) by incorporating two types of relative-type stopping criteria for solving the subproblems. The proposed inexact framework has considerable flexibility to encompass many existing exact and inexact methods, and can accommodate different types of errors that may occur when solving the subproblem. This enables the potential application of our inexact framework across different DC decompositions to facilitate the design of a more efficient DCA scheme in practice. The global subsequential convergence and the global sequential convergence of our iBPDCA are established under suitable conditions including the Kurdyka–Lojasiewicz property. Some numerical experiments on the 1-2 regularized least squares problem and the constrained 1-2 sparse optimization problem are conducted to show the superior performance of our iBPDCA in comparison to existing algorithms. These results also empirically validate the necessity and significance of developing different types of stopping criteria to facilitate the efficient computation of the subproblem in each iteration of our iBPDCA. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part

关键词： Convex optimization

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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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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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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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Artificial Intelligence in Health Care computing and Assessments in Oman: an Effectual Study 2

Artificial Intelligence in Health Care Computing and Assessm...

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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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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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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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