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UltraSegNet:A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

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Computers, Materials & Continua 2025年第5期83卷 3303-3333页

作者： Suhaila Abuowaida Hamza Abu Owida Deema Mohammed Alsekait Nawaf Alshdaifat Diaa Salama Abd Elminaam Mohammad Alshinwan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdallah II for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Medical Engineering Department Faculty of EngineeringAl-Ahliyya Amman UniversityAmman19328Jordan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Faculty of Computers Science Misr International UniversityCairo11800Egypt Jadara Research Center Jadara UniversityIrbid21110Jordan

Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image *** paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations:This work adds three things:(1)a changed ResNet-50 backbone with sequential 3×3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries;(2)a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory;and(3)an adaptive feature fusion strategy that changes local and global featuresbasedonhowthe image isbeing *** evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance:On the BUSI dataset,it obtains a precision of 0.915,a recall of 0.908,and an F1 score of *** the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of ***,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU ***,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of *** is a huge improvement over traditional methods that have a hard time with small-scale features,as standard models can only achieve 0.63–0.71 *** improvement in small lesion detection is particularly crucial for early-stage breast cancer *** from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.

关键词： Breast cancer ultrasound image segmentation classification deep learning

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Adaptive High-order Synchroextracting Transform with time-varying parameter and its Application in Gearbox Fault Diagnosis

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IEEE Sensors Journal 2025年第10期25卷 17867-17880页

作者： Xu, Kai Wang, Dongxiao Wu, Xing Liu, Xiaoqin Kunming University of Science Faculty of Mechanical and Electrical Engineering Kunming650500 China Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province Kunming650500 China West Yunnan University of Applied Sciences Dali671000 China

To address the limitations of existing time-frequency analysis (TFA) techniques, including low time-frequency resolution and fixed window parameters when analyzing strongly time-varying signals, this study proposes an adaptive higher-order synchroextracting transform (AHSET) with time-varying parameters. The method reduces the approximation error of instantaneous frequency (IF) estimation through performing higher-order Taylor expansion of the signal phase and amplitude. The aggregation quality in the time-frequency representation (TFR) is quantified through minimizing Rényi entropy, thereby enabling the adaptive selection of optimal window parameters for each frequency band. Building on this framework, an adaptive high-order synchroextracting operator (AHSEO) is constructed that achieves both precise IF estimation and TFR throughout the entire period. Simulation analyses show that compared with the traditional TFA method, AHSET offers superior time-frequency resolution and IF estimation accuracy when processing strong frequency modulation signals, while also demonstrating enhanced noise robustness across various signal-to-noise ratios (SNR). In addition, the angular domain IF fluctuation analysis is proposed for gearbox fault diagnosis based on the estimated current IF by AHSET. This method relates the periodic IF fluctuation of motor current with the meshing period of faulty tooth, and improves the reliability of fault diagnosis. The experimental analysis confirm that the proposed method can accurately extract the fault features under time-varying operating conditions and successfully diagnose the faults of the RV gearbox. © 2001-2012 IEEE.

关键词： Signal to noise ratio

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Enhancing Privacy in IoT Networks: A Comparative Analysis of Classification and Defense Methods

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IEEE Access 2025年 13卷 71611-71646页

作者： Emre Ergun, Ahmet Can, Ozgu Kantarcioglu, Murat Ege University Graduate School of Natural and Applied Sciences Bornova35100 Turkey Izmir Katip Celebi University Faculty of Engineering and Architecture Department of Computer Engineering Cigli35620 Turkey Ege University Department of Computer Engineering Bornova35100 Turkey Virginia Tech Department of Computer Science BlacksburgVA24061 United States

The rapid proliferation of Internet of Things (IoT) devices has led to a substantial increase in network packet traffic, raising significant privacy concerns. Although traffic encryption is employed to protect the privacy of IoT devices, attackers can still leverage Machine Learning (ML) and Deep Learning (DL) techniques to classify device types by analyzing packet characteristics, such as size and timing. The main challenges in the state of the art are the lack of effective methods for exposing privacy violations in encrypted IoT traffic, and the absence of robust defense mechanisms to mitigate privacy breaches caused by network traffic analysis. Considering these challenges, this study presents two key contributions: (i) a novel vector-based classification method that enhances device-type identification from encrypted IoT traffic using advanced ML and DL techniques, and (ii) a robust defense mechanism based on Differential Privacy (DP) and advanced padding techniques against traffic analysis attacks. Therefore, the study examines privacy risks associated with sequential IoT device data and evaluates the effectiveness of ML algorithms using two datasets. The results demonstrate that the proposed vector-based classification method significantly improves the attacker’s classification accuracy, even when privacy-preserving techniques, such as padding, are used to obscure device-type classification. For this purpose, the study evaluates eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for IoT traffic classification, achieving an accuracy rate of 99.61% with XGBoost, 96.74% with LSTM, and 96.94% with GRU. Additionally, the Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (kNN), and GRU classification algorithms are also evaluated and compared with the XGBoost and LSTM classifiers for the proposed attack model. As a defense mechanism, DP is applied using the Fourier Perturbation Algorithm (FPA) to optimize padd

关键词： Decision trees

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PVC reinforced with Pb1/2Ni1/2Fe2O4 spinel ferrites nanoparticles: development of linear/nonlinear optical, dielectric characteristics and γ-rays buildup-factors of for optical and radiation safety applications

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Optical and Quantum Electronics 2025年第1期57卷 1-21页

作者： Alfryyan, Nada Alsaif, Norah A. M. Khattari, Z.Y. Al-Ghamdi, Hanan Elsad, R.A. Shams, M.S. El-Refaey, Adel M. Abouhaswa, A.S. Elamy, Mamdouh I. Rammah, Y.S. Department of Physics College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Physics Faculty of Science The Hashemite University P. O. Box 330127 Zarqa13133 Jordan Basic Engineering Science Department Faculty of Engineering Menoufia University Shebin El-Koom32511 Egypt Department of Basic and Applied Science Collage of Engineering and Technology Arab Academy of Science Technology and Maritime Transport Smart Village Giza Egypt Physics and Mathematical Engineering Department Faculty of Electronic Engineering Menoufia University 32952 Menouf Egypt Department of Physics Faculty of Science Menoufia University Shebin El-Koom32511 Egypt Institute of Natural Science and Mathematics Ural Federal University Ekaterinburg620002 Russia Industrial Engineering Department College of Engineering Northern Border University Arar Saudi Arabia Department of Production Engineering and Mechanical Design Faculty of Engineering Menoufia University Shebin El‑Kom Egypt Pharos University in Alexandria Canal El Mahmoudia Street Beside Green Plaza Complex Alexandria21648 Egypt

Physical, linear/nonlinear optical, dielectric characteristics, and γ-ray-buildup factors of nanocomposites (NCs) sheets of PVC doped with x(Pb0.5Ni0.5Fe2O4) spinel nanoferrites: x = 0–10 wt% and named as PVC/PNF0-PVC/PNF10 have been tested. Density (ρ) changed from 1.405 g·cm−3 to 2.6130 g·cm−3 as x changed from 0.0 to 10 wt%. Optical-energy-gap (Eoptical) diminished from 5.207 to 2.753 eV, whereas the linear index of refraction of (nlinear) increased from 1.962 to 2.405 for PVC/PNF0 and PVC/PNF10 NCs sheets. The nonlinear optical susceptibility (χ3) and nonlinear refractive index (n2optical) of the investigated PVC/PNF-NCs sheets increased as (Pb0.5Ni0.5Fe2O4) content increased. Dielectric spectroscopy of fabricated PVC/PNF-NCs is investigated throughout frequencies from 50 Hz to 5 × 106 Hz at RT. The dielectric-constant was reduced by introduction of Pb0.5Ni0.5Fe2O4. Mass absorption coefficient (MAC) values of PVC/PNF-NCs sheets obeyed the fact that (PVC/PNF0)MAC MAC MAC MAC MAC. The γ-ray exposure, and absorption-buildup factors of the investigated PVC/PNF-NCs sheets followed as (PVC/PNF0)EBF/EABF > (PVC/PNF1)EBF/EABF > (PVC/PNF3)EBF/EABF > (PVC/PNF5)EBF/EABF > (PVC/PNF10)EBF/EABF. Results confirmed that the suggested PVC/PNF-NCs sheets with smallest dielectric constant may implement in electrical cable. Also, PVC/PNF-NCs sheets may implement optical, electrical devices and γ-ray attenuation field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Gamma ray production

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Adaptive Parallel Average Schwarz Preconditioner for Reduced Hsieh-Clouh-Tocher Macro Element 15th

Adaptive Parallel Average Schwarz Preconditioner for Reduce...

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15th International Conference on Parallel Processing and applied Mathematics, PPAM 2024

作者： Marcinkowski, Leszek Rahman, Talal Faculty of Mathematics Informatics and Mechanics University of Warsaw Banacha 2 Warszawa02-097 Poland Faculty of Engineering and Science Western Norway University of Applied Sciences Inndalsveien 28 Bergen5063 Norway

ISBN: (纸本)9783031857027

The paper describes and analyzes an Average Schwarz Method with spectrally enriched coarse space for a reduced Hsieh-Clough-Tocher (RHCT) finite element discretization of a 4th-order elliptic multiscale problem. The derived symmetric preconditioner is applied and the PCG iterative method is used to solve the preconditioned problem. Suppose the enrichments of the coarse space contain sufficiently many specially constructed eigenfunctions. In that case, the convergence rate of the PCG method is weakly dependent on the ratio of the coarse to fine mesh h/H. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Finite element method

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Weak and Extremely Low Frequency Magnetic Fields Alter the Growth Rates of HT-1080 Fibrosarcoma

Weak and Extremely Low Frequency Magnetic Fields Alter the G...

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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Dang, Nhat Keller, Jason Bajtos, Marek Gurhan, Hakki Barnes, Frank Department of Electrical Computer and Energy Engineering College of Engineering and Applied Science University of Colorado Boulder BoulderCO United States Department of Electromagnetics and Biomedical Engineering Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovakia

ISBN: (纸本)9781946815200

This study presents experimental results showing that weak, extremely low frequency electromagnetic (EM) fields influence the growth rates of HT-1080 fibrosarcoma cells in vitro and offers a theoretical explanation for these effects. The results demonstrate that weak magnetic fields both increase and decrease the growth rates of fibrosarcoma cells by altering the mitochondrial superoxide (O2-) and calcium ions (Ca2+) concentrations in living cells. Over a 4-day exposure period, fibrosarcoma growth rates were modulated in the presence of low-level alternating current (AC) magnetic fields at a 10 μT amplitude at 13.5 Hz to 30 Hz frequency, superimposed on a 45 μT static background field. Effects due to changes in amplitude from 10 to 30 μT at selected frequencies are also investigated with the same superimposed static field. © 2025 USNC-URSI.

关键词： Electromagnetic field effects

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Comparison of Standard Ant Colony Optimization, Dijkstra and A∗ Algorithms for Path Planning of Robot in an Indoor Environment

Comparison of Standard Ant Colony Optimization, Dijkstra and...

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9th International Conference on Mechanical engineering and Robotics Research, ICMERR 2025

作者： Sharan, Sudeep Ngoc Minh, Anh Tong Dominguez-Jimenez, Juan Jose Nauth, Peter Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cadiz Spain

ISBN: (纸本)9798331509262

The research topic of Path planning is extremely challenging area of concentration within the field of mobile robots. However, path planning algorithms for mobile robot tasks are contingent upon the environment and its level of complexity. This paper analyzes four distinct path planning methods for simulated indoor environment. The proposed algorithms include conventional Ant Colony methods, Ant System (AS), Ant Colony System (ACS), as well as standard methods Dijkstra and AStar (A*). We analyzed and examined these algorithms by employing various metrics/maps with complexity. The results indicate that the traditional path planning algorithm Dijkstra and A∗ approaches surpass the other ant colony techniques in terms of both computation time and path distance. © 2025 IEEE.

关键词： Ant colony optimization

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A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm

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Journal of Experimental and Theoretical Artificial Intelligence 2025年第2期37卷 307-342页

作者： Tabagchi Milan, Sara Jafari Navimipour, Nima Lohi Bavil, Hamed Yalcin, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Engineering University of Applied Sciences & Technology Tabriz Iran Department of Computer Engineering Nisantasi University Istanbul Turkey

Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cloud computing

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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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Exact Soliton Solutions and Dynamical Behaviors of Hausdorff Fractal Fokas–Lenells Equation

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International Journal of applied and Computational Mathematics 2025年第2期11卷 1-20页

作者： Abdelkhalek, A. El-Kalla, I.L. Elsaid, A. Kader, A. H. Abdel Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University PO 35516 Mansoura Egypt Department of Mathematics Institute of Basic and Applied Sciences Egypt-Japan University of Science and Technology (E-JUST) New Borg El-Arab City Alexandria Egypt Mathematics Department Faculty of Science New Mansoura University New Mansoura City Egypt

In this paper, the Hausdorff Fractal Fokas-Lenells (HFFL) equation with full nonlinearity is investigated. The travelling wave reduction technique is utilized to transform the HFFL equation into an ordinary differential equation that can be converted to a two-dimensional planar dynamical system. By applying the direct integration method, some doubly periodic solutions are obtained for the HFFL equation in the form of Jacobi elliptic functions and Weierstrass function. New peakon, kink, bright, dark, W-shaped, and M-shaped solitons solutions for the HFFL equation are found under some constraints. The two-dimensional planar dynamical system is investigated to calculate the equilibrium points, which are classified as hyperbolic and nonhyperbolic equilibrium points. The theories of dynamical systems are employed to analyze the stability of individual equilibrium points as well as the bifurcation of the system's dynamics. Vector fields are plotted in the two-dimensional phase portraits to explore the dynamics near each equilibrium point. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Bifurcation Doubly periodic Fokas–Lenells Hausdorff fractal Nonlinear dynamics Solitons

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