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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yue, Yubiao Xue, Jun Liang, Haihuang Li, Zhenzhang Wang, Yufeng School of Biomedical Engineering Guangzhou Medical University Guangzhou China School of Computer Science and Technology Anhui University Hefei China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou China Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350368741

Due to the lack of effective mpox detection tools, the mpox virus continues to spread worldwide and has been once again declared a public health emergency of international concern by the World Health Organization. Lightweight deep learning model-based detection systems are crucial to alleviate mpox outbreaks since they are suitable for widespread deployment, especially in resource-limited scenarios. However, the key to its successful application depends on ensuring that the model can effectively model local features and long-range dependencies in mpox lesions while maintaining lightweight. Inspired by the success of Mamba in modeling long-range dependencies and its linear complexity, we proposed a lightweight hybrid architecture called MpoxMamba for efficient mpox detection. MpoxMamba utilizes depth-wise separable convolutions to extract local feature representations in mpox skin lesions and greatly enhances the model's ability to model the global contextual information by grouped Mamba modules. Notably, MpoxMamba's parameter size and FLOPs are 0.77M and 0.53G, respectively. Experimental results on two widely recognized benchmark datasets demonstrate that MpoxMamba outperforms state-of-the-art lightweight models and existing mpox detection methods. Importantly, we developed a web-based online application to provide free mpox detection. The source code of MpoxMamba and the developed online application are available at https://***/YubiaoYue/MpoxMamba. © 2025 IEEE.

关键词： Deep Leraning Lightweight Model Mpox Detection Vision Mamba

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FedDGA: Federated Multitask Learning Based on Dynamic Guided Attention

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 268-280页

作者： Sun, Haoyun Zhao, Hongwei Xu, Liang Zhang, Weishan Guan, Hongqing Yang, Su China University of Petroleum Qingdao266580 China Shandong Data Open Innovative Application Laboratory Qingdao266580 China WINDAKA Technology Company Ltd. Qingdao266580 China Beijing University of Science and Technology Beijing100083 China Swansea University Department of Computer Science Faculty of Science and Engineering SwanseaSA2 8PP United Kingdom

The proliferation of privacy-sensitive data has spurred the development of federated learning (FL), which is an important technology for state-of-the-art machine learning and responsible AI. However, most existing FL methods are constrained in their applicability and generalizability due to their narrow focus on specific tasks. This article presents a novel federated multitask learning (FMTL) framework that is capable of acquiring knowledge across multiple tasks. To address the challenges posed by non-IID data and task imbalance in FMTL, this study proposes a federated fusion strategy based on dynamic guided attention (FedDGA), which adaptively fine-tunes local models for multiple tasks with personalized attention. In addition, this article designed dynamic batch weight (DBW) to balance the task losses and improve the convergence speed. Extensive experiments were conducted on various datasets, tasks, and settings, and the proposed method was compared with state-of-the-art methods such as FedAvg, FedProx, and SCAFFOLD. The results show that our method achieves significant performance gains, with up to 11.1% increase in accuracy over the baselines. © 2020 IEEE.

关键词： Scaffolds

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Sampling-Based Motion Planning for Guide Robots Considering User Pose Uncertainty 25th

Sampling-Based Motion Planning for Guide Robots Considering...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Mosquera-Maturana, Juan Sebastian Hernández Vega, Juan David Romero Cano, Victor Faculty of Engineering Universidad Autónoma de Occidente Cali Colombia School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

ISBN: (纸本)9783031720581

In this paper, we propose a framework to address the problem of guiding a person within a semi-structured environment in a socially acceptable manner that prioritises safety and comfort. We propose an algorithm based on the optimal Rapidly exploring Random Tree (RRT*) algorithm for path planning. Our proposal utilises Dubins curves and takes into account the user during path planning to generate a navigation path that allows the robot to follow a feasible path that can also be navigated by the user. A comparative analysis against standard path planning based on the RRT* algorithm and the Social Force Model validates the efficacy of our proposed algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Microrobots

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Current and Voltage Calculations of the 400 kV Transmission Line Using Laboratory Measurements and computer Simulation 33rd

Current and Voltage Calculations of the 400 kV Transmission...

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Torma, Matej Fekete, Krešimir Kljajić, Ružica Čvek, Kristijan Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Kneza Trpimira 2b Osijek31000 Croatia

ISBN: (纸本)9783031805967

In this paper, the current, voltages, as well as active and reactive power of the 400 kV transmission line are calculated using three different methods. The objective of the paper is to demonstrate and compare three different approaches that are based on laboratory measurement, analytical approach, and computer simulation. Measurements are carried out on the laboratory model of a real-life 400 kV transmission line while analytical approach is conducted using a short line model with lumped parameters. As a last approach, the transmission line is modeled in a computer program for power flow analysis, and power flow simulations with the same parameters as for the previous two methods are performed. All three methods are used to calculate voltages, currents, and powers at the beginning and at the end of the transmission line for a few scenarios. Results are compared and discussed in order to demonstrate the possibilities of the applied approaches for the education of power system engineers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Power transmission lines

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Dual-Channel MEMS Actuated Tunable Optical Filter Based on Polarization Insensitive Compound Ring Resonator for DWDM Networks 11th

Dual-Channel MEMS Actuated Tunable Optical Filter Based on P...

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11th EAI International Conference on Advancement of science and Technology, ICAST 2023

作者： Ayalew, Hailu Dessalegn Talabattula, Srinivas Annamalai, Pushparaghavan Electrical and Computer Engineering Faculty Bahir Dar University-Institute of Technology Bahir Dar Ethiopia Department of Electrical Communication Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9783031641503

One of the key components of wavelength division multiplexing (WDM) networks is tunable optical filters (TOFs). This paper focused on the theoretical analysis and design of a new dual-channel micro-electromechanical system (MEMS) actuated tunable optical filter based on polarization insensitive compound ring resonators (PICRRs) for dense wavelength division multiplexing (DWDM) optical networking applications. It is basically structured on an optimally designed capacitive type microcantilever beam, which is fixed at both ends, with built-in PICRRs, which are placed at both fixed ends of the beam to gain linearly varying longitudinal strain and stress on the resonators. Also, the dimensions of the rings are properly chosen to achieve a full width at half maximum (FWHM) of 0.4078 nm with a flat pass band at 1 dB of 0.3626 nm and a good shape factor for both channels. It also shows a free spectral range of about 20.05 nm for the first channel and 20.046 nm for the second channel, with a high extinction ratio that allows filtering of adjacent channels of 50 GHz DWDM systems. Thus, when the electrostatic attraction force (EAF) is applied, the beam will bend, which induces strain and stress in the rings, which brings a change in effective refractive index of the rings and also affects the perimeter and coupling coefficients of the rings that leads to a change in the output spectrum shift, providing the tunability as high as 4.779 nm/μN, and it is capable of tuning up to 355 GHz with negligible variations of FWHM and channel spacing within the tunability range of the two channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dense wavelength division multiplexing

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Smartphone Continuous Authentication via Deep Behavioral Features and Isolation Forest 24

Smartphone Continuous Authentication via Deep Behavioral Fea...

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3rd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Ma, Hangyuan Li, Zhongrui Mou, Shiyu School of Computer Science and Artificial Intelligence Southwest Minzu University Sichuan Chengdu China Engineering Faculty Biomedical Engineering Department University of Malaya Petaling Jaya Malaysia

ISBN: (纸本)9798400710834

Smartphone authentication typically relies on user input of passwords or biometric features such as facial recognition, fingerprint scanning, or iris recognition. However, these methods only authenticate the user at specific points in time, meaning that once the password is compromised or the device is unlocked, unauthorized individuals can access all information on the device or perform unauthorized actions. To resolve this issue, continuous authentication has emerged, continuously verifying the user’s identity by covertly monitoring the ongoing interactions between the user and the device. This paper proposes a continuous authentication method that utilizes smartphone sensor data to extract users’ deep behavioral features through Siamese Convolutional Neural Networks (Siamese CNN). These features are then input into the Isolation Forest (iForest) algorithm for classification. Experimental results demonstrate that the proposed method can authenticate the user within one second, achieving an average accuracy of 93.79% and an average Equal Error Rate (EER) of 8.37%, significantly outperforming other comparative methods used in this study. © 2024 Copyright held by the owner/author(s).

关键词： Smartphones

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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

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SN computer science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks 4

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Ahmed, Marzia Mohamad, Ahmad Johari Hassan, Md Maruf Bhuiyan, Touhid Department of Software Engineering Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Malaysia Southeast University Department of Computer Science and Engineering Dhaka Bangladesh School of IT VA United States

ISBN: (纸本)9798331518882

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN's hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model's potential for accurate real-time air quality forecasting, offering a robust, sustainable solution. © 2025 IEEE.

关键词： Air Quality GBO Metaheuristic Neural Network Time series forecasting

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A Comparative Analysis of Liquid Neural Networks and Incremental Learning Approaches for Stock Market Prediction

A Comparative Analysis of Liquid Neural Networks and Increme...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Jammal, Hussein Srour, Farah Salman, Ali Owayjan, Michel Ayoub, Omran Rottondi, Cristina Achkar, Roger American University of Science & Technology Faculty of Engineering and Computer Science Beriut Lebanon Politecnico di Torino Italy Nantes University Saint-Nazaire France University of Applied Sciences of Southern Switzerland Switzerland

ISBN: (纸本)9798331529604

Due to the complexity, challenge, and fast growth of stock markets, they encourage more efficient use of financial resources and the expansion of macroeconomics. Tesla and Apple stock prices fluctuate constantly due to the impact of various industries and market conditions. The extraction of previously unknown patterns and information from time series is central to numerous real-world applications. This paper presents a comparative study of stock market prediction techniques for Tesla and Apple stocks, focusing primarily on the application of Liquid Neural Networks (LNN) and evaluating their performance against Incremental Learning methods. Employing LNN, a novel approach in the field of time-series forecasting, we developed models that leverage the dynamic and flexible structure of LNN to adapt to the complex and non-linear patterns observed in stock prices. The performance of the LNN model was quantitatively assessed using standard metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy. The LNN model achieved an MSE of 0.000317, RMSE of 0.0178, MAE of 0.014131, MAPE of 1.8%, and a Directional Accuracy of 49.36%. In contrast, the Incremental Learning approach yielded an MSE of 0.54355, RMSE of 0.73725, MAE of 0.73158, MAPE of 89.39%, and Directional Accuracy of 28.37%. These results underscore the superior accuracy and effectiveness of LNNs in modeling stock market behaviors compared to traditional Incremental Learning methods. The findings not only demonstrate the potential of LNNs in financial analytics but also suggest avenues for further research into enhancing predictive accuracy and model robustness. © 2025 IEEE.

关键词： Contrastive Learning

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A Framework for Real-Time Monitoring of Power Consumption of Distributed Calculation on Computational Cluster 15

A Framework for Real-Time Monitoring of Power Consumption of...

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15th International Conference on Cloud Computing and Services science, CLOSER 2025

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology al. 1000-lecia PP 7 Kielce Poland

ISBN: (纸本)9789897587474

This paper proposes a framework for real-time monitoring of the power consumption of distributed calculation on the nodes of the cluster. The framework allows to visualize and analyze the provider results based on the context information about the performed calculation. The first part of the framework is devoted to monitoring the power consumption during the execution of machine learning algorithms and the performance of NoSQL storage. The second part is dedicated to the testing of distributed data storage, called Scalable Distributed Two-Layered Data Structure (SD2DS). The results show that the framework can be used in the development of a management system that could schedule computations to take full advantage of renewable energy. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Cluster computing

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