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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhang, Boqiang He, Weiting Wang, Ruofei Lin, Jin Kuang, Huiyu School of Computers Neusoft Institute Guangdong Foshan528000 China

ISBN: (纸本)9781510689053

Fundus images are a valuable tool in the diagnosis of ophthalmic and systemic diseases. However, the traditional manual analysis process is time-consuming, labour-intensive and susceptible to subjective factors, which presents a challenge in the context of efficient and accurate diagnosis. It is therefore imperative to develop efficient and automated classification methods to enhance the accuracy and efficiency of diagnosis. In this experiment, a disease classification method combining multi-scale feature fusion with a spatial-channel attention mechanism (CBAM) is proposed for the automated classification of fundus images. The method employs GoogLeNet as the underlying network and utilises the Inception module to facilitate parallel extraction of multi-scale features, thereby enabling the effective capture of multi-level feature information. Furthermore, it is combined with the CBAM module, which enables the dynamic adjustment of weights for feature maps in both the channel and spatial dimensions. This significantly enhances the network's capacity to recognise disease features. The experimental results demonstrate that the enhanced model attains a classification accuracy of 92.64% on the publicly accessible OIA-ODIR dataset. This represents a notable enhancement in terms of evaluation metrics, including accuracy, precision, and F1 score, in comparison to the original GoogLeNet, VGG19, and ResNet50 models. Although the recall rate was slightly lower than that of ResNet50, the overall performance was excellent, thereby validating the effectiveness of the method in fundus disease classification and providing strong support for automated fundus disease diagnosis. © 2025 SPIE.

关键词： Diseases and disorders data modeling Feature extraction Performance modeling Education and training Eye models Image classification Glaucoma Cataracts Neurological disorders

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Examining the Relationship Between Hand Gesture Recognition Accuracy and data Quality Indices for EMG and pFMG Signals: A Correlation analysis

Examining the Relationship Between Hand Gesture Recognition ...

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2025 IEEE International conference on Mechatronics, ICM 2025

作者： Zhang, Shen Zhou, Hao Tchantchane, Rayane Alici, Gursel Group University of Wollongong Faculty of Engineering and Information Sciences WollongongNSW2522 Australia

ISBN: (纸本)9798331533892

Accurate recognition of hand gestures is crucial especially for in prosthetic control and human-machine interactions, and other applications. This study explores the correlation between data quality indices including Signal-to-Noise Ratio (SNR) and Signal-to-Motion Artifact Ratio (SMR), and hand gesture recognition accuracy, utilizing Surface Electromyography (sEMG) and pressure-based Force Myography (pFMG) signals. Through this investigation, we demonstrate that prioritizing SNR in sEMG signals and SMR in FMG signals when positioning the sensors has the potential to improve hand gesture recognition performance, as our analysis showed that SNR has a higher correlation with hand gesture accuracy with sEMG signals while SMR has a higher correlation with hand gesture accuracy with pFMG signals. The findings highlight the critical role that comprehensive data quality assessments play in improving the accuracy of the hand gesture recognition and the reliability of prosthetic control systems, for example. By enhancing the signal quality for both sEMG and pFMG, this study provides new insights into sensor positioning and channel selection for the development of more efficient and precise gesture recognition systems for assistive devices. It emphasizes the necessity of utilizing comprehensive data quality assessment and lays the foundation for future enhancements in gesture recognition accuracy by addressing signal contamination or quality challenges. © 2025 IEEE.

关键词： Signal to noise ratio

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NeuroPhysNet: A Novel Hybrid Neural Network Model for Enhanced Prediction and control of Cyber-Physical Systems 19th

NeuroPhysNet: A Novel Hybrid Neural Network Model for Enhanc...

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19th International conference on Mathematical modeling and Simulation of Systems, MODS 2024

作者： Dolhopolov, Serhii Honcharenko, Tetyana Chernyshev, Denys Kyiv National University of Construction and Architecture 31 Air Force Avenue Kyiv03037 Ukraine

ISBN: (纸本)9783031907340

This paper introduces NeuroPhysNet, a novel hybrid neural network architecture designed for enhanced prediction and control of cyber-physical systems (CPS). The increasing complexity and dynamic nature of CPS present significant challenges in modeling and control, often stretching the limits of traditional approaches. NeuroPhysNet addresses these challenges by seamlessly integrating deep learning techniques with physics-based modeling, offering a robust framework that leverages both data-driven insights and domain-specific knowledge. The architecture comprises three main components: a Deep Neural Network (DNN) module for capturing complex non-linear relationships, a Physical Model Integration (PMI) module for incorporating domain-specific equations, and an Adaptive Fusion (AF) module for dynamically combining outputs based on current system states. We evaluate NeuroPhysNet across multiple CPS domains, including smart grid energy management, autonomous vehicle control, and industrial process control. Results demonstrate significant improvements in prediction accuracy, physical consistency, and generalization capabilities compared to state-of-the-art baseline models. NeuroPhysNet achieves up to 15% improvement in mean squared error and a 20% increase in physical consistency scores across tested domains. This research contributes to the growing field of physics-informed machine learning, offering a versatile approach for enhancing the performance and reliability of complex cyber-physical systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Physical addresses

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Smart Swimming Training: Wearable Body Sensor Networks Empower Technical Evaluation of Competitive Swimming

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 4448-4465页

作者： Li, Jie Wang, Jiaxin Qiu, Sen Liu, Xiaofeng Li, Jianqing Xiang, Wentao Liu, Bin Zhu, Songsheng Kiong, Loo Chu Cangelosi, Angelo Fortino, Giancarlo Nanjing Med Univ Sch Biomed Engn & Informat Nanjing 211166 Peoples R China Hebei Univ Technol Sch Mech Engn Hebei Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Hohai Univ Coll Artificial Intelligence & Automat Changzhou 213200 Peoples R China Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia Univ Manchester Sch Comp Sci Manchester M13 9RL England Univ Calabria Dept Informat Modeling Elect & Syst I-87036 Arcavacata Di Rende Italy

The combination of wearable sensors and competitive sports provides quantitative information for scientific training, effectively assisting athletes in improving their athletic performance. This study presents a technical framework for athletic sports assessment in competitive swimming based on body-area sensor networks. In our approach, wearable inertial sensor nodes are placed on specific body parts of the athletes to capture motion data during different competitive swimming strokes. Multiwearable inertial sensor nodes are worn on specific body parts of athletes for real-time monitoring motion data during training sessions. A motion intensity detection-based error-state-Kalman-filter algorithm is proposed for multisensor data fusion. Additionally, through kinematic statistical analysis, the characteristics of joint motion during training are clearly explained. Furthermore, a deep learning network that fuses sensor time series and human skeleton graphs is proposed for different stroke phase segmentation, enabling quantitative measurement of motion phases, and several baseline classifiers are chosen for comparison to validate the robustness of our phase segmentation method. We also investigate the sensor combination selection issue during the phase segmentation process to determine the optimal sensor configuration. Our approach provides a scientific solution for the integration of wearable sensors and competitive sports, contributing to the high-quality development of the next generation of smart sports.

关键词： Body sensor network (BSN) competitive swimming motion capture motion capture multisensor data fusion multisensor data fusion phase segmentation phase segmentation phase segmentation

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5th International conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

5th International Conference on Innovative Product Design an...

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5th International conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

ISBN: (纸本)9789819767311

The proceedings contain 57 papers. The special focus in this conference is on Innovative Product Design and Intelligent Manufacturing Systems. The topics include: Development of an Intuitive Autonomous Ground Robot for Indoor Delivery Applications;development of a Computer-Assisted Vision-Based Inspection Monitoring System for Quality Assurance;analysis of Challenges of IoT Adoption for Smart City Application;finite Element Modelling and Comparative Assessment of Single-Level Cervical Disc Replacement with Fusion Surgery;a Statistical Method-Based Auto-Fitted Genetic Algorithm for Damage Diagnosis;emovere Agnitio by Textual Tweets Using Machine Learning;government Bidding Security Management System Using Blockchain;resource Forecasting of Geographical Area Using Big data Analytics;design Optimization and Aerothermodynamic analysis Over a Supersonic Vehicle;Evaluation of Weldability and Optimization of TIG Welding process Parameters for Maximized Yield Strength;terahertz-Frequency Propagation for Autonomous Transport Infrastructure;energy-Efficient Coffee Pulper;community Perception on Groundwater Quality Versus Analytical Quality in the Coastal Areas of Visakhapatnam, India—Opinion Versus Actuality;simulation modeling and analysis for Supply Chain Disruptions: A Case Study from a process Industry in Kerala;integrating Hand Gesture Tracking with Physical and Digital Twins of Robotic Arm;performance Enhancement of Pump Parameters Through Innovative Spring Design of Mechanical Seal for a Heat Exchanger;enhancing Biocompatibility and Performance of Multi-directionally Expanding Materials for Dental Applications: Design and analysis;synthesis of Hybrid Nanofluids, Properties, and Its Applications—A Comprehensive Review;application of Hybrid Decision-Making Method in Ranking Polymer Biomaterials for Interim Dental Crowns;analysis on Mechanical Properties of Aluminium MMC with Graphite and Boron Nitride Powder;textile Crafts of Bharat.

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4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

4th International Conference on Numerical Computations: Theo...

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4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

ISBN: (纸本)9783031812439

The proceedings contain 105 papers. The special focus in this conference is on Numerical Computations: Theory and Algorithms. The topics include: Resource Allocation via Bayesian Optimization: an Efficient Alternative to Semi-Bandit Feedback;multi-Objective and Multiple Information Source Optimization for Fair & Green Machine Learning;extended Optimal control Problem for Practical Application;explainable process Deviance Discovery with data-Efficient Deep Learning;line Search Stochastic Gradient Algorithm with A-priori Rule for Monitoring the control of the Variance;a Machine Learning Approach to Speed up the Solution of the Distributor’s Pallet Loading Problem;combined First- and Second-Order Directions for Deep Neural Networks Training;constrained Global Optimization by Smoothing;the Unreasonable Effectiveness of Optimal Transport Distance in the Design of Multi-Objective Evolutionary Optimization Algorithms;an Improved Modified Jaya Optimization Algorithm: Application to the Solution of Nonlinear Equation Systems;GPU Acceleration of the Enhanced Jaya Optimization Algorithm for Solving Large Systems of Nonlinear Equations;effective Resistance Based Community Detection in Complex Networks;a Comparison of Formulations for Aircraft Deconfliction;optimal Recombination Problem in Genetic Programming for Boolean Functions;heuristics with Local Improvements for Two-processor Scheduling Problem with Energy Constraint and Parallelization;numerical analysis of Optimal control of Assets and Liabilities by a Bank;optimal control for Stochastic Multi-agent Systems With the Use of Parallel Hybrid Genetic Algorithm;DC Optimization in Adversarial Sparse Support Vector Machine;A First-Order Optimality Condition in Nonsmooth Generalized Semi-infinite Programming (GSIP);miniaturisation of Binary Classifiers Through Sparse Neural Networks;Price Forecasting for Bitcoin: Linear Regression and SVM Approaches;inside the Box: 0–1 Linear Programming Under Interval Uncertainty;machine Learn

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4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

4th International Conference on Numerical Computations: Theo...

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4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

ISBN: (纸本)9783031812408

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Framework for Drug User analysis and Prediction 4

Framework for Drug User Analysis and Prediction

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4th International conference on Sentiment analysis and Deep Learning, ICSADL 2025

作者： Sethuraman, Dhanush Ananya, Ela Joseph Jeffrey, J. Thangavel, Senthil Kumar Rangasami, P. Somasundaram, K. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Amrita Vishwa Vidyapeetham Department of Social Work Coimbatore India Amrita School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

This paper provides a framework for knowledge and action related to and about substance abuse, that integrates both capacity development and data-driven analysis for drug use analysis and modeling. In line with the National Action Plan for Drug Demand Reduction (NAPDDR) the framework focuses on a multi-pronged strategy. It then gives examples of so-called capacity-building exercises, such as those carried out by Amrita Vishwa Vidyapeetham in Tamil Nadu in 2020. These programs targeted key stakeholders, including healthcare professionals, police personnel, teachers, and social workers, focusing on preventive education, medical management, psychosocial issues, and rehabilitation strategies related to substance abuse. Pre- and post-training evaluations showed a strong increase in participants knowledge and treatment skills for substance use disorders. Second, the paper presents a data-driven, quantitative analysis of drug use patterns and related factors. Using a Support Vector Machine (SVM) algorithm, the study utilizes a dataset of drug consumption patterns to determine the factors which are most important for drug consumption. Importantly, the framework also include Explainable AI (XAI) methods to increase the interpretability and tracibility of the machine learning model. XAI approaches are used to interpret the model's decision process and to identify the most important factors in predicting the use of drugs. Incorporating insights from the capacity-building program evaluation and from the XAI-aided machine learning analysis, this framework provides an integrated end-to-end framework for drug user analysis and the generation of actionable recommendations for the design of targeted interventions and prevention strategies. © 2025 IEEE.

关键词： Support vector machines

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Time Series analysis and Rule Mining for Detecting Industrial control System data Injection Attacks 5th

Time Series Analysis and Rule Mining for Detecting Industria...

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5th International conference on Computing and Network Communications, CoCoNet 2023

作者： Mehmood, Merwa Baig, Zubair Syed, Naeem Deakin University Geelong Australia

ISBN: (纸本)9789819745395

Cyber-physical systems (CPSs) are complex interconnections of control systems that operate in tandem to carry out industrial processes and activities. A research problem of interest is the lack of cyber-attack models for a CPS, which hinders the design of robust defense mechanisms. We aim to generate variant attack patterns through the application of association rule mining to a commonly available dataset, namely SWaT. Our methodology comprises the generation of synthetic attack variants to foster the development of robust intrusion detection models. Synthetic samples created by this process can be used to create adversarial samples, which have not yet been addressed in the field of CPS. In this work, we have also generated more than six hundred classification rules and applied time series analysis to validate potential data injection attack variants for the SWaT dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

4th International Conference on Numerical Computations: Theo...

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4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

ISBN: (纸本)9783031812460

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