There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reac...There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.展开更多
Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often f...Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development.展开更多
The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threa...The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.展开更多
The present investigation inspects the unsteady,incompressible MHD-induced flow of a ternary hybrid nanofluid made of SiO_(2)(silicon dioxide),ZnO(zinc oxide),and MWCNT(multi-walled carbon nanotubes)suspended in a wat...The present investigation inspects the unsteady,incompressible MHD-induced flow of a ternary hybrid nanofluid made of SiO_(2)(silicon dioxide),ZnO(zinc oxide),and MWCNT(multi-walled carbon nanotubes)suspended in a water-ethylene glycol base fluid between two perforated squeezing Riga plates.This problem is important because it helps us understand the complicated connections between magnetic fields,nanofluid dynamics,and heat transport,all of which are critical for designing thermal management systems.These findings are especially useful for improving the design of innovative cooling technologies in electronics,energy systems,and healthcare applications.No prior study has been done on the theoretical study of the flow of ternary nanofluid(SiO_(2)+ZnO+MWCNT/Water−EthylGl ycol,(60∶40))past a pierced squeezed Riga plates using the boundary value problem solver 4th-order collocation(BVP4C)numerical approach to date.So,the current work has been carried out to fill this gap,and the core purpose of this study is to explore the aspects that enhance the heat transfer of base fluids(H_(2)O/EG)suspended with three nanomaterials SiO_(2),ZnO,and MWCNT.The Riga plates introduce electromagnetic forcing through an embedded array of magnets and electrodes,generating Lorentz forces to regulate the flow.The squeezing effect introduces dynamic boundary movement,which enhances mixing;however,permeability,due to porosity,replicates the true material limits.Similarity transformations of the Navier-Stokes and energy equations result in a highly nonlinear set of ordinary differential equations that govern momentum and thermal energy transport.The subsequent boundary value problem is solved utilizing the BVP4C numerical approach.The study observes the impact of magnetic parameters,squeezing velocity,solid volume percentages of the three nanoparticles,and porous medium factors on velocity and temperature fields.Results show that magnetic fields reduce the velocity profile by 6.75%due to increased squeezing and medium effects.Tri-hybrid nanofluids notice a 9%rise in temperature with higher thermal radiation.展开更多
This study introduces an Edge-Based Data Hiding and Extraction Algorithm(EBDHEA)to address the problem of data embedding in images while preserving robust security and high image quality.The algorithm produces three c...This study introduces an Edge-Based Data Hiding and Extraction Algorithm(EBDHEA)to address the problem of data embedding in images while preserving robust security and high image quality.The algorithm produces three classes of pixels from the pixels in the cover image:edges found by the Canny edge detection method,pixels arising from the expansion of neighboring edge pixels,and pixels that are neither edges nor components of the neighboring edge pixels.The number of Least Significant Bits(LSBs)that are used to hide data depends on these classifications.Furthermore,the lossless compression method,Huffman coding,improves image data capacity.To increase the security of the steganographic process,secret messages are encrypted using the XOR encryption technique before being embedded.Metrics such as the Mean Squared Error(MSE),Peak Signal-to-Noise Ratio(PSNR),and Structural Similarity Index Measure(SSIM)are used to assess the efficacy of this algorithm and are compared to previous methods.The findings demonstrate that the suggested approach achieves high similarity between the original and modified images with a maximum PSNR of 60.7 dB for a payload of 18,750 bytes,a maximum SSIM of 0.999 for a payload of 314,572.8 bytes,and a maximum Video Information Fidelity(VIF)of 0.95 for a payload of 23,592 bytes.Normalized Cross-Correlation(NCC)values are very close to 1.In addition,the performance of EBDHEA is implemented on Secure Medical Image Transmission as a real-world example,and the performance is tested against three types of attacks:RS Steganalysis,Chi-square attack,and visual attack,and compared with two deep learning models,such as SRNet and XuNet.展开更多
In recent years,hepatology has undergone a transformative evolution driven by significant advancements in diagnostic and therapeutic technologies.The expanding integration of endoscopic modalities into hepatology has ...In recent years,hepatology has undergone a transformative evolution driven by significant advancements in diagnostic and therapeutic technologies.The expanding integration of endoscopic modalities into hepatology has enforced the diagnosis,staging,management of liver diseases beside integration into transplantation.This review highlights the evolving discipline of“endo-hepatology”,where endoscopic ultrasound,endoscopic retrograde cholangiopancreatography,and novel interventional tools are employed to address the critical challenges in chronic liver disease.The review provides a comprehensive synthesis of current evidence and different clinical applications,while also exploring future directions including revolution of artificial intelligence-assisted endoscopies and enhanced imaging endoscopies.By bridging the anatomical and functional interface between the gastrointestinal lumen and the liver,endo-hepatology is not only improving diagnostic accuracy and therapeutic precision but also reshaping multidisciplinary paradigms in hepatology practice.展开更多
This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Select...This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Selection(AHB-AHS)method.A finite element dynamic equation for the AEDR system is introduced,considering complex nonlinearities of the intershaft bearing,unbalanced excitations,and high-frequency excitation.A solving strategy combining the AHB-AHS method and improved arclength continuation method is proposed to solve highdimensional dynamic equations containing complex nonlinearities and to track periodic solutions with parameter variations.The Floquet theory is used to analyze the types of bifurcation points in the system and the stability of periodic motions.The results indicate that high-frequency excitation can couple high-order and low-order modes,especially when the system undergoes superharmonic resonance.High-frequency excitation leads to more combination frequency harmonics,among which N_(f)ω_(1)-2ω_(2)dominates.Furthermore,changing the parameters(amplitude and frequency)of high-frequency excitation widens or shifts the unstable regions of the system.These findings contribute to understanding the mechanism of high-frequency excitation on aero-engines and demonstrate that the proposed AHB-AHS method is a powerful tool for analyzing highdimensional complex nonlinear dynamic systems under multi-frequency excitation.展开更多
Intermittent fasting(IF)has emerged as a potential adjunctive strategy in cancer prevention,mitigation,and treatment.This narrative review synthesizes existing literature to explore the relationship between IF and can...Intermittent fasting(IF)has emerged as a potential adjunctive strategy in cancer prevention,mitigation,and treatment.This narrative review synthesizes existing literature to explore the relationship between IF and cancer across 3 dimensions:prevention,mitigation,and treatment.We examine the underlying mechanisms by which IF may reduce cancer risk,including its effects on insulin-like growth factor 1 suppression,autophagy induction,and chronic inflammation reduction.Furthermore,we discuss IF’s potential to enhance the efficacy of conventional cancer therapies by sensitizing cancer cells,promoting apoptosis,and reducing treatmentrelated side effects.While promising,the evidence is still limited,and further research is required to elucidate the long-term impact and optimal implementation of IF in cancer care.This review aims to provide health care professionals and patients with a comprehensive understanding of the potential benefits and risks associated with IF as a complementary approach in the oncology setting.展开更多
To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the ...To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional reservoirs.It does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both groups.Mahalanobis distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water flooding.Predictive models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and effectiveness.The LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training group.That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction performance.This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil production.It also establishes a new standard for such forecasting,which can lead to the development of more effective and sustainable solutions for oil recovery.展开更多
This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with ...This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with the Lagrangian mechanics,representing a two-degree-of-freedom nonlinear electromechanical system subject to harmonic base excitation under a 1:1 internal resonance condition.The model is normalized,and the conditions dictating monostable and bistable oscillation modes are identified.The bifurcation characteristics of the coupled system are analyzed in both oscillation modes by means of harmonic balance and continuation methods.The vibration isolation performance,with and without the coupled harvester,is evaluated in terms of displacement transmissibility to assess its dual functionalities for vibration isolation and energy harvesting.Analytical results demonstrate that integrating a piezoelectric harvester into a monostable QZS isolator under 1:1 internal resonance does not compromise its vibration isolation capability while enabling efficient energy harvesting at extremely low-frequency base excitation.Furthermore,the system's response under strong base excitation is investigated exclusively for energy harvesting in both monostable and bistable modes,leading to optimal structural parameter design.The conditions for intra-well and inter-well periodic oscillation modes,as well as chaotic responses,are analyzed analytically and validated numerically through stability charts,basins of attraction,bifurcation diagrams,time histories,and Poincarémaps.This work provides a comprehensive understanding of the oscillation dynamics of QZS isolators and offers valuable insights for optimizing their geometric parameters to function as high-performance vibration isolators and/or energy harvesters.展开更多
Enhancing the efficiency of Rankine cycles is crucial for improving the performance of thermal power plants,as it directly impacts operational costs and emissions in light of energy transition goals.This study sets it...Enhancing the efficiency of Rankine cycles is crucial for improving the performance of thermal power plants,as it directly impacts operational costs and emissions in light of energy transition goals.This study sets itself apart from existing research by applying a novel optimization technique to a basic ideal Rankine cycle,focusing on a specific power plant that has not been previously analyzed.Currently,this cycle operates at 41%efficiency and a steam quality of 76%,constrained by fixed operational parameters.The primary objectives are to increase thermal efficiency beyond 46%and raise steam quality above 85%,while adhering to operational limits:a boiler pressure not exceeding 15 MPa,condenser pressure not dropping below 10 kPa,and turbine temperature not surpassing 500℃.This study utilizes numerical simulations to model the effects of varying boiler pressure(Pb)and condenser pressure(Pc)within the ranges of 12MPa<Pb<15 MPa and 5 kPa<Pc<10 kPa.By systematically adjusting these parameters,the proposed aimto identify optimal conditions that maximize efficiency and performance within specified constraints.The findings will provide valuable insights for power plant operators seeking to optimize performance under real-world conditions,contributing to more efficient and sustainable power generation.展开更多
Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes.This study introduces a hybrid ap...Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes.This study introduces a hybrid approach integrating Long Short-Term Memory(LSTM)networks with the Hybrid Greylag Goose and Particle Swarm Optimization(GGPSO)algorithm to optimize preterm birth classification using Electrohysterogram signals.The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings,capturing key physiological features such as contraction patterns,entropy,and statistical variations.Statistical analysis and feature selection methods are applied to identify the most relevant predictors and enhance model interpretability.LSTM networks effectively capture temporal patterns in uterine activity,while the GGPSO algorithm finetunes hyperparameters,mitigating overfitting and improving classification accuracy.The proposed GGPSO-optimized LSTM model achieved superior performance with 97.34%accuracy,96.91%sensitivity,97.74%specificity,and 97.23%F-score,significantly outperforming traditional machine learning approaches and demonstrating the effectiveness of hybrid metaheuristic optimization in enhancing deep learning models for clinical applications.By combining deep learning withmetaheuristic optimization,this study contributes to advancing intelligent auto-diagnosis systems,facilitating early detection of pretermbirth risks and timely medical interventions.展开更多
Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling...Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.展开更多
Objective:To assess knowledge and practice among nurses toward neonatal resuscitation in hospitals in Hebron.Methods:A cross-sectional study design was conducted among 151 nurses from Hebron.Data were collected using ...Objective:To assess knowledge and practice among nurses toward neonatal resuscitation in hospitals in Hebron.Methods:A cross-sectional study design was conducted among 151 nurses from Hebron.Data were collected using a self-administered questionnaire.Multiple logistic regression was conducted to test the associated factors with the dependent variables.Results:Only 9.3%of participants had a standardized level of knowledge,while 68.9%reported adequate skills for neonatal resuscitation.Factors associated with a standardized level of knowledge were age(p=0.001)and supportive supervision(p=0.007).Factors associated with the standardized practice level were the presence of hospital guidelines(p=0.028),previous training of nurses(p=0.011),and the presence of supportive supervision(p<0.001).Conclusions:The majority of emergency nurses in Hebron hospitals demonstrated a substandard level of knowledge of neonatal resuscitation.However,most of those nurses surprisingly reported a standardized practice level.The presence of hospital guidelines,training programs,or direct supervision for nurses are factors that can be considered to improve both knowledge and practice toward neonatal resuscitation among emergency nurses in Hebron.展开更多
With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heurist...With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains,such as machine learning,process control,and engineering design,showcasing their capability to address complex optimization problems.The Stochastic Fractal Search(SFS)algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials.Since its introduction by Hamid Salimi in 2015,SFS has garnered significant attention from researchers and has been applied to diverse optimization problems acrossmultiple disciplines.Its popularity can be attributed to several factors,including its simplicity,practical computational efficiency,ease of implementation,rapid convergence,high effectiveness,and ability to address singleandmulti-objective optimization problems,often outperforming other established algorithms.This review paper offers a comprehensive and detailed analysis of the SFS algorithm,covering its standard version,modifications,hybridization,and multi-objective implementations.The paper also examines several SFS applications across diverse domains,including power and energy systems,image processing,machine learning,wireless sensor networks,environmental modeling,economics and finance,and numerous engineering challenges.Furthermore,the paper critically evaluates the SFS algorithm’s performance,benchmarking its effectiveness against recently published meta-heuristic algorithms.In conclusion,the review highlights key findings and suggests potential directions for future developments and modifications of the SFS algorithm.展开更多
Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image quality.This paper presents the UltraSegNet architecture that addres...Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image quality.This 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 used.Extensive 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 0.911.In the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of 0.894.Importantly,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU hardware.Notably,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of 0.891.This 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 accuracy.This improvement in small lesion detection is particularly crucial for early-stage breast cancer identification.Results from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.展开更多
Early and accurate cancer diagnosis through medical imaging is crucial for guiding treatment and enhancing patient survival.However,many state-of-the-art deep learning(DL)methods remain opaque and lack clinical interp...Early and accurate cancer diagnosis through medical imaging is crucial for guiding treatment and enhancing patient survival.However,many state-of-the-art deep learning(DL)methods remain opaque and lack clinical interpretability.This paper presents an explainable artificial intelligence(XAI)framework that combines a fine-tuned Visual Geometry Group 16-layer network(VGG16)convolutional neural network with layer-wise relevance propagation(LRP)to deliver high-performance classification and transparent decision support.This approach is evaluated on the publicly available Kaggle kidney cancer imaging dataset,which comprises labeled cancerous and noncancerous kidney scans.The proposed model achieved 98.75%overall accuracy,with precision,recall,and F1-score each exceeding 98%on an independent test set.Crucially,LRP-derived heatmaps consistently localize anatomically and pathologically significant regions such as tumor margins in agreement with established clinical criteria.The proposed framework enhances clinician trust by delivering pixel-level justifications alongside state-of-the-art predictive performance.It facilitates informed decision-making,thereby addressing a key barrier to the clinical adoption of DL in oncology.展开更多
We propose an effective surface plasmon resonance system designed to achieve both negative and positive Goos–H??nchen shifts in reflected light.This system comprises a metal film and an underlying medium,where the re...We propose an effective surface plasmon resonance system designed to achieve both negative and positive Goos–H??nchen shifts in reflected light.This system comprises a metal film and an underlying medium,where the real part of the permittivity of the underlying medium must be less than unity.Surface plasmon polaritons can be excited at the lower surface of the metal when light is incident from the air onto the upper surface of the metal.The excitation of surface plasmon polaritons leads to the exploration of the Goos–H??nchen shift(G–HS).Control over the negative and positive(G–HS)is investigated via the wavelength of the incident light.The magnitude of the G–HS is strongly dependent on the incident wavelength.A remarkable enhancement of both negative and positive G–HS in the reflected light is achieved at certain wavelengths and incident angles.Our system paves the way for exploring different characteristics of optical switching and micro-sensors with very high precision.展开更多
Based on the density functional theory,the double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb,and Ta)were studied to predict their structural,thermodynamic,thermoelectric,and optical characteristics.All the considered ...Based on the density functional theory,the double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb,and Ta)were studied to predict their structural,thermodynamic,thermoelectric,and optical characteristics.All the considered alloys are thermodynamically stable and have semiconductor behavior with indirect band gaps of 0.62,0.75,and 0.8 eV for LuVCo_(2)Bi_(2),LuNbCo_(2)Bi_(2),and LuTaCu_(2)Bi_(2),respectively.The investigated compounds exhibit semiconducting behavior with energy gaps below 0.8 eV.The impact of heat and pressure on thermodynamic coefficients was evaluated,and the influence of charge carriers on the temperature-dependent properties was studied using the semi-classical Boltzmann model.The studied compounds were characterized by their low lattice thermal conductivity at room temperature and low thermal expansion coefficient.These alloys exhibit substantial absorption coefficients in the ultraviolet(UV)light region,high optical conductivity,and high reflectivity in the visible light region,making them highly appealing materials for applications in the energy and electronics sectors.展开更多
The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure t...The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure to sophisticated cyber threats.The diversity of devices,high data volumes,and real-time operational demands complicate security,requiring not just robust intrusion detection but also effective feature selection for relevance and scalability.Traditional Machine Learning(ML)based Intrusion Detection System(IDS)improves detection but often lacks interpretability,limiting stakeholder trust and timely responses.Moreover,centralized feature selection in conventional IDS compromises data privacy and fails to accommodate the decentralized nature of smart city infrastructures.To address these limitations,this research introduces an Interpretable Federated Learning(FL)based Cyber Intrusion Detection model tailored for smart city applications.The proposed system leverages privacy-preserving feature selection,where each client node independently identifies top-ranked features using ML models integrated with SHAP-based explainability.These local feature subsets are then aggregated at a central server to construct a global model without compromising sensitive data.Furthermore,the global model is enhanced with Explainable AI(XAI)techniques such as SHAP and LIME,offering both global interpretability and instance-level transparency for cyber threat decisions.Experimental results demonstrate that the proposed global model achieves a high detection accuracy of 98.51%,with a significantly low miss rate of 1.49%,outperforming existing models while ensuring explainability,privacy,and scalability across smart city infrastructures.展开更多
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea(No.RS-2025-02315209).
文摘There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.
文摘Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development.
文摘The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.
基金funded by King Saud University,Riyadh,Saudi Arabia,through the Ongo-ing Research Funding program—Research Chairs(ORF-RC-2025-0127)funded via Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R443).
文摘The present investigation inspects the unsteady,incompressible MHD-induced flow of a ternary hybrid nanofluid made of SiO_(2)(silicon dioxide),ZnO(zinc oxide),and MWCNT(multi-walled carbon nanotubes)suspended in a water-ethylene glycol base fluid between two perforated squeezing Riga plates.This problem is important because it helps us understand the complicated connections between magnetic fields,nanofluid dynamics,and heat transport,all of which are critical for designing thermal management systems.These findings are especially useful for improving the design of innovative cooling technologies in electronics,energy systems,and healthcare applications.No prior study has been done on the theoretical study of the flow of ternary nanofluid(SiO_(2)+ZnO+MWCNT/Water−EthylGl ycol,(60∶40))past a pierced squeezed Riga plates using the boundary value problem solver 4th-order collocation(BVP4C)numerical approach to date.So,the current work has been carried out to fill this gap,and the core purpose of this study is to explore the aspects that enhance the heat transfer of base fluids(H_(2)O/EG)suspended with three nanomaterials SiO_(2),ZnO,and MWCNT.The Riga plates introduce electromagnetic forcing through an embedded array of magnets and electrodes,generating Lorentz forces to regulate the flow.The squeezing effect introduces dynamic boundary movement,which enhances mixing;however,permeability,due to porosity,replicates the true material limits.Similarity transformations of the Navier-Stokes and energy equations result in a highly nonlinear set of ordinary differential equations that govern momentum and thermal energy transport.The subsequent boundary value problem is solved utilizing the BVP4C numerical approach.The study observes the impact of magnetic parameters,squeezing velocity,solid volume percentages of the three nanoparticles,and porous medium factors on velocity and temperature fields.Results show that magnetic fields reduce the velocity profile by 6.75%due to increased squeezing and medium effects.Tri-hybrid nanofluids notice a 9%rise in temperature with higher thermal radiation.
文摘This study introduces an Edge-Based Data Hiding and Extraction Algorithm(EBDHEA)to address the problem of data embedding in images while preserving robust security and high image quality.The algorithm produces three classes of pixels from the pixels in the cover image:edges found by the Canny edge detection method,pixels arising from the expansion of neighboring edge pixels,and pixels that are neither edges nor components of the neighboring edge pixels.The number of Least Significant Bits(LSBs)that are used to hide data depends on these classifications.Furthermore,the lossless compression method,Huffman coding,improves image data capacity.To increase the security of the steganographic process,secret messages are encrypted using the XOR encryption technique before being embedded.Metrics such as the Mean Squared Error(MSE),Peak Signal-to-Noise Ratio(PSNR),and Structural Similarity Index Measure(SSIM)are used to assess the efficacy of this algorithm and are compared to previous methods.The findings demonstrate that the suggested approach achieves high similarity between the original and modified images with a maximum PSNR of 60.7 dB for a payload of 18,750 bytes,a maximum SSIM of 0.999 for a payload of 314,572.8 bytes,and a maximum Video Information Fidelity(VIF)of 0.95 for a payload of 23,592 bytes.Normalized Cross-Correlation(NCC)values are very close to 1.In addition,the performance of EBDHEA is implemented on Secure Medical Image Transmission as a real-world example,and the performance is tested against three types of attacks:RS Steganalysis,Chi-square attack,and visual attack,and compared with two deep learning models,such as SRNet and XuNet.
文摘In recent years,hepatology has undergone a transformative evolution driven by significant advancements in diagnostic and therapeutic technologies.The expanding integration of endoscopic modalities into hepatology has enforced the diagnosis,staging,management of liver diseases beside integration into transplantation.This review highlights the evolving discipline of“endo-hepatology”,where endoscopic ultrasound,endoscopic retrograde cholangiopancreatography,and novel interventional tools are employed to address the critical challenges in chronic liver disease.The review provides a comprehensive synthesis of current evidence and different clinical applications,while also exploring future directions including revolution of artificial intelligence-assisted endoscopies and enhanced imaging endoscopies.By bridging the anatomical and functional interface between the gastrointestinal lumen and the liver,endo-hepatology is not only improving diagnostic accuracy and therapeutic precision but also reshaping multidisciplinary paradigms in hepatology practice.
基金the financial support from the National Key R&D Program of China(No.2023YFE0125900)National Natural Science Foundation of China(Nos.12372008 and 12102234)+1 种基金Natural Science Foundation of Heilongjiang Province,China(No.YQ2022A008)Taif University,Saudi Arabia,for supporting this work through Project number(TU-DSPP-2024-73).
文摘This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Selection(AHB-AHS)method.A finite element dynamic equation for the AEDR system is introduced,considering complex nonlinearities of the intershaft bearing,unbalanced excitations,and high-frequency excitation.A solving strategy combining the AHB-AHS method and improved arclength continuation method is proposed to solve highdimensional dynamic equations containing complex nonlinearities and to track periodic solutions with parameter variations.The Floquet theory is used to analyze the types of bifurcation points in the system and the stability of periodic motions.The results indicate that high-frequency excitation can couple high-order and low-order modes,especially when the system undergoes superharmonic resonance.High-frequency excitation leads to more combination frequency harmonics,among which N_(f)ω_(1)-2ω_(2)dominates.Furthermore,changing the parameters(amplitude and frequency)of high-frequency excitation widens or shifts the unstable regions of the system.These findings contribute to understanding the mechanism of high-frequency excitation on aero-engines and demonstrate that the proposed AHB-AHS method is a powerful tool for analyzing highdimensional complex nonlinear dynamic systems under multi-frequency excitation.
文摘Intermittent fasting(IF)has emerged as a potential adjunctive strategy in cancer prevention,mitigation,and treatment.This narrative review synthesizes existing literature to explore the relationship between IF and cancer across 3 dimensions:prevention,mitigation,and treatment.We examine the underlying mechanisms by which IF may reduce cancer risk,including its effects on insulin-like growth factor 1 suppression,autophagy induction,and chronic inflammation reduction.Furthermore,we discuss IF’s potential to enhance the efficacy of conventional cancer therapies by sensitizing cancer cells,promoting apoptosis,and reducing treatmentrelated side effects.While promising,the evidence is still limited,and further research is required to elucidate the long-term impact and optimal implementation of IF in cancer care.This review aims to provide health care professionals and patients with a comprehensive understanding of the potential benefits and risks associated with IF as a complementary approach in the oncology setting.
文摘To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional reservoirs.It does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both groups.Mahalanobis distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water flooding.Predictive models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and effectiveness.The LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training group.That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction performance.This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil production.It also establishes a new standard for such forecasting,which can lead to the development of more effective and sustainable solutions for oil recovery.
基金Project supported by the National Key R&D Program of China(No.2023YFE0125900)。
文摘This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with the Lagrangian mechanics,representing a two-degree-of-freedom nonlinear electromechanical system subject to harmonic base excitation under a 1:1 internal resonance condition.The model is normalized,and the conditions dictating monostable and bistable oscillation modes are identified.The bifurcation characteristics of the coupled system are analyzed in both oscillation modes by means of harmonic balance and continuation methods.The vibration isolation performance,with and without the coupled harvester,is evaluated in terms of displacement transmissibility to assess its dual functionalities for vibration isolation and energy harvesting.Analytical results demonstrate that integrating a piezoelectric harvester into a monostable QZS isolator under 1:1 internal resonance does not compromise its vibration isolation capability while enabling efficient energy harvesting at extremely low-frequency base excitation.Furthermore,the system's response under strong base excitation is investigated exclusively for energy harvesting in both monostable and bistable modes,leading to optimal structural parameter design.The conditions for intra-well and inter-well periodic oscillation modes,as well as chaotic responses,are analyzed analytically and validated numerically through stability charts,basins of attraction,bifurcation diagrams,time histories,and Poincarémaps.This work provides a comprehensive understanding of the oscillation dynamics of QZS isolators and offers valuable insights for optimizing their geometric parameters to function as high-performance vibration isolators and/or energy harvesters.
文摘Enhancing the efficiency of Rankine cycles is crucial for improving the performance of thermal power plants,as it directly impacts operational costs and emissions in light of energy transition goals.This study sets itself apart from existing research by applying a novel optimization technique to a basic ideal Rankine cycle,focusing on a specific power plant that has not been previously analyzed.Currently,this cycle operates at 41%efficiency and a steam quality of 76%,constrained by fixed operational parameters.The primary objectives are to increase thermal efficiency beyond 46%and raise steam quality above 85%,while adhering to operational limits:a boiler pressure not exceeding 15 MPa,condenser pressure not dropping below 10 kPa,and turbine temperature not surpassing 500℃.This study utilizes numerical simulations to model the effects of varying boiler pressure(Pb)and condenser pressure(Pc)within the ranges of 12MPa<Pb<15 MPa and 5 kPa<Pc<10 kPa.By systematically adjusting these parameters,the proposed aimto identify optimal conditions that maximize efficiency and performance within specified constraints.The findings will provide valuable insights for power plant operators seeking to optimize performance under real-world conditions,contributing to more efficient and sustainable power generation.
基金funded by the National Plan for Science,Technology and Innovation(MAARIFAH)-King Abdulaziz City for Science and Technology-The Kingdom of Saudi Arabia-award number(13-MAT377-08).
文摘Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes.This study introduces a hybrid approach integrating Long Short-Term Memory(LSTM)networks with the Hybrid Greylag Goose and Particle Swarm Optimization(GGPSO)algorithm to optimize preterm birth classification using Electrohysterogram signals.The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings,capturing key physiological features such as contraction patterns,entropy,and statistical variations.Statistical analysis and feature selection methods are applied to identify the most relevant predictors and enhance model interpretability.LSTM networks effectively capture temporal patterns in uterine activity,while the GGPSO algorithm finetunes hyperparameters,mitigating overfitting and improving classification accuracy.The proposed GGPSO-optimized LSTM model achieved superior performance with 97.34%accuracy,96.91%sensitivity,97.74%specificity,and 97.23%F-score,significantly outperforming traditional machine learning approaches and demonstrating the effectiveness of hybrid metaheuristic optimization in enhancing deep learning models for clinical applications.By combining deep learning withmetaheuristic optimization,this study contributes to advancing intelligent auto-diagnosis systems,facilitating early detection of pretermbirth risks and timely medical interventions.
基金the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1567).
文摘Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.
文摘Objective:To assess knowledge and practice among nurses toward neonatal resuscitation in hospitals in Hebron.Methods:A cross-sectional study design was conducted among 151 nurses from Hebron.Data were collected using a self-administered questionnaire.Multiple logistic regression was conducted to test the associated factors with the dependent variables.Results:Only 9.3%of participants had a standardized level of knowledge,while 68.9%reported adequate skills for neonatal resuscitation.Factors associated with a standardized level of knowledge were age(p=0.001)and supportive supervision(p=0.007).Factors associated with the standardized practice level were the presence of hospital guidelines(p=0.028),previous training of nurses(p=0.011),and the presence of supportive supervision(p<0.001).Conclusions:The majority of emergency nurses in Hebron hospitals demonstrated a substandard level of knowledge of neonatal resuscitation.However,most of those nurses surprisingly reported a standardized practice level.The presence of hospital guidelines,training programs,or direct supervision for nurses are factors that can be considered to improve both knowledge and practice toward neonatal resuscitation among emergency nurses in Hebron.
基金supported by Prince Sattam bin Abdulaziz University for funding this research work through the project number(2024/RV/06).
文摘With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains,such as machine learning,process control,and engineering design,showcasing their capability to address complex optimization problems.The Stochastic Fractal Search(SFS)algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials.Since its introduction by Hamid Salimi in 2015,SFS has garnered significant attention from researchers and has been applied to diverse optimization problems acrossmultiple disciplines.Its popularity can be attributed to several factors,including its simplicity,practical computational efficiency,ease of implementation,rapid convergence,high effectiveness,and ability to address singleandmulti-objective optimization problems,often outperforming other established algorithms.This review paper offers a comprehensive and detailed analysis of the SFS algorithm,covering its standard version,modifications,hybridization,and multi-objective implementations.The paper also examines several SFS applications across diverse domains,including power and energy systems,image processing,machine learning,wireless sensor networks,environmental modeling,economics and finance,and numerous engineering challenges.Furthermore,the paper critically evaluates the SFS algorithm’s performance,benchmarking its effectiveness against recently published meta-heuristic algorithms.In conclusion,the review highlights key findings and suggests potential directions for future developments and modifications of the SFS algorithm.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R435),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image quality.This 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 used.Extensive 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 0.911.In the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of 0.894.Importantly,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU hardware.Notably,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of 0.891.This 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 accuracy.This improvement in small lesion detection is particularly crucial for early-stage breast cancer identification.Results from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.
基金supported through the Ongoing Research Funding Program(ORF-2025-498),King Saud University,Riyadh,Saudi Arabia.
文摘Early and accurate cancer diagnosis through medical imaging is crucial for guiding treatment and enhancing patient survival.However,many state-of-the-art deep learning(DL)methods remain opaque and lack clinical interpretability.This paper presents an explainable artificial intelligence(XAI)framework that combines a fine-tuned Visual Geometry Group 16-layer network(VGG16)convolutional neural network with layer-wise relevance propagation(LRP)to deliver high-performance classification and transparent decision support.This approach is evaluated on the publicly available Kaggle kidney cancer imaging dataset,which comprises labeled cancerous and noncancerous kidney scans.The proposed model achieved 98.75%overall accuracy,with precision,recall,and F1-score each exceeding 98%on an independent test set.Crucially,LRP-derived heatmaps consistently localize anatomically and pathologically significant regions such as tumor margins in agreement with established clinical criteria.The proposed framework enhances clinician trust by delivering pixel-level justifications alongside state-of-the-art predictive performance.It facilitates informed decision-making,thereby addressing a key barrier to the clinical adoption of DL in oncology.
基金Hubei University of Automotive Technology through the start-up research grant(BK202212),located in Shiyan 442002,China。
文摘We propose an effective surface plasmon resonance system designed to achieve both negative and positive Goos–H??nchen shifts in reflected light.This system comprises a metal film and an underlying medium,where the real part of the permittivity of the underlying medium must be less than unity.Surface plasmon polaritons can be excited at the lower surface of the metal when light is incident from the air onto the upper surface of the metal.The excitation of surface plasmon polaritons leads to the exploration of the Goos–H??nchen shift(G–HS).Control over the negative and positive(G–HS)is investigated via the wavelength of the incident light.The magnitude of the G–HS is strongly dependent on the incident wavelength.A remarkable enhancement of both negative and positive G–HS in the reflected light is achieved at certain wavelengths and incident angles.Our system paves the way for exploring different characteristics of optical switching and micro-sensors with very high precision.
文摘Based on the density functional theory,the double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb,and Ta)were studied to predict their structural,thermodynamic,thermoelectric,and optical characteristics.All the considered alloys are thermodynamically stable and have semiconductor behavior with indirect band gaps of 0.62,0.75,and 0.8 eV for LuVCo_(2)Bi_(2),LuNbCo_(2)Bi_(2),and LuTaCu_(2)Bi_(2),respectively.The investigated compounds exhibit semiconducting behavior with energy gaps below 0.8 eV.The impact of heat and pressure on thermodynamic coefficients was evaluated,and the influence of charge carriers on the temperature-dependent properties was studied using the semi-classical Boltzmann model.The studied compounds were characterized by their low lattice thermal conductivity at room temperature and low thermal expansion coefficient.These alloys exhibit substantial absorption coefficients in the ultraviolet(UV)light region,high optical conductivity,and high reflectivity in the visible light region,making them highly appealing materials for applications in the energy and electronics sectors.
文摘The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure to sophisticated cyber threats.The diversity of devices,high data volumes,and real-time operational demands complicate security,requiring not just robust intrusion detection but also effective feature selection for relevance and scalability.Traditional Machine Learning(ML)based Intrusion Detection System(IDS)improves detection but often lacks interpretability,limiting stakeholder trust and timely responses.Moreover,centralized feature selection in conventional IDS compromises data privacy and fails to accommodate the decentralized nature of smart city infrastructures.To address these limitations,this research introduces an Interpretable Federated Learning(FL)based Cyber Intrusion Detection model tailored for smart city applications.The proposed system leverages privacy-preserving feature selection,where each client node independently identifies top-ranked features using ML models integrated with SHAP-based explainability.These local feature subsets are then aggregated at a central server to construct a global model without compromising sensitive data.Furthermore,the global model is enhanced with Explainable AI(XAI)techniques such as SHAP and LIME,offering both global interpretability and instance-level transparency for cyber threat decisions.Experimental results demonstrate that the proposed global model achieves a high detection accuracy of 98.51%,with a significantly low miss rate of 1.49%,outperforming existing models while ensuring explainability,privacy,and scalability across smart city infrastructures.
