Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food proce...Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest,storage,preparation,and consumption.This current paper seeks to demystify the importance of artificial intelligence,machine learning(ML),deep learning(DL),and computer vision(CV)in ensuring food safety and quality.By stressing the importance of these technologies,the audience will feel reassured and confident in their potential.These are very handy for such problems,giving assurance over food safety.CV is incredibly noble in today's generation because it improves food processing quality and positively impacts firms and researchers.Thus,at the present production stage,rich in image processing and computer visioning is incorporated into all facets of food production.In this field,DL and ML are implemented to identify the type of food in addition to quality.Concerning data and result-oriented perceptions,one has found similarities regarding various approaches.As a result,the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered.It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further.Also,DL is accurately integrated with identifying the quality and safety of foods in the market.This paper describes the current practices and concerns of ML,DL,and probable trends for its future development.展开更多
The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)...The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.展开更多
Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart d...Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart disease,but more remains to be accomplished.The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches.By using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single feature.We processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data quality.Furthermore,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the feature.To reduce the dimensionality,we subsequently used PCA with 95%variation.To identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble models.The model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested approach.This illustrates how interaction-focused feature analysis can produce precise and useful insights for heart disease diagnosis.展开更多
Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints...Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints,notably the no-cloning theorem,which prohibits the exact duplication of unknown quantum states and has profound implications for cryptography,secure communication,and error correction.While existing quantum circuit representations implicitly honor such constraints,they lack formal mechanisms for early-stage verification in software design.Addressing this constraint at the design phase is essential to ensure the correctness and reliability of quantum software.This paper presents a formal metamodeling framework using UML-style notation and and Object Constraint Language(OCL)to systematically capture and enforce the no-cloning theorem within quantum software models.The proposed metamodel formalizes key quantum concepts—such as entanglement and teleportation—and encodes enforceable invariants that reflect core quantum mechanical laws.The framework’s effectiveness is validated by analyzing two critical edge cases—conditional copying with CNOT gates and quantum teleportation—through instance model evaluations.These cases demonstrate that the metamodel can capture nuanced scenarios that are often mistaken as violations of the no-cloning theorem but are proven compliant under formal analysis.Thus,these serve as constructive validations that demonstrate the metamodel’s expressiveness and correctness in representing operations that may appear to challenge the no-cloning theorem but,upon rigorous analysis,are shown to comply with it.The approach supports early detection of conceptual design errors,promoting correctness prior to implementation.The framework’s extensibility is also demonstrated by modeling projective measurement,further reinforcing its applicability to broader quantum software engineering tasks.By integrating the rigor of metamodeling with fundamental quantum mechanical principles,this work provides a structured,model-driven approach that enables traditional software engineers to address quantum computing challenges.It offers practical insights into embedding quantum correctness at the modeling level and advances the development of reliable,error-resilient quantum software systems.展开更多
A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,...A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.展开更多
The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare I...The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.展开更多
Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitiv...Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.展开更多
Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates dise...Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates disease severity,supports effective treatment strategies,and reduces reliance on excessive pesticide use.Traditional machine learning approaches have been applied for automated rice disease diagnosis;however,these methods depend heavily on manual image preprocessing and handcrafted feature extraction,which are labor-intensive and time-consuming and often require domain expertise.Recently,end-to-end deep learning(DL) models have been introduced for this task,but they often lack robustness and generalizability across diverse datasets.To address these limitations,we propose a novel end-toend training framework for convolutional neural network(CNN) and attention-based model ensembles(E2ETCA).This framework integrates features from two state-of-the-art(SOTA) CNN models,Inception V3 and DenseNet-201,and an attention-based vision transformer(ViT) model.The fused features are passed through an additional fully connected layer with softmax activation for final classification.The entire process is trained end-to-end,enhancing its suitability for realworld deployment.Furthermore,we extract and analyze the learned features using a support vector machine(SVM),a traditional machine learning classifier,to provide comparative insights.We evaluate the proposed E2ETCA framework on three publicly available datasets,the Mendeley Rice Leaf Disease Image Samples dataset,the Kaggle Rice Diseases Image dataset,the Bangladesh Rice Research Institute dataset,and a combined version of all three.Using standard evaluation metrics(accuracy,precision,recall,and F1-score),our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis,with potential applicability to other agricultural disease detection tasks.展开更多
The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary gro...The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.展开更多
Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin...Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.展开更多
In modern construction,Lightweight Aggregate Concrete(LWAC)has been recognized as a vital material of concern because of its unique properties,such as reduced density and improved thermal insulation.Despite the extens...In modern construction,Lightweight Aggregate Concrete(LWAC)has been recognized as a vital material of concern because of its unique properties,such as reduced density and improved thermal insulation.Despite the extensive knowledge regarding its macroscopic properties,there is a wide knowledge gap in understanding the influence of microscale parameters like aggregate porosity and volume ratio on the mechanical response of LWAC.This study aims to bridge this knowledge gap,spurred by the need to enhance the predictability and applicability of LWAC in various construction environments.With the help of advanced numerical methods,including the finite element method and a random circular aggregate model,this study critically evaluates the role played by these microscale factors.We found that an increase in the aggregate porosity from 23.5%to 48.5%leads to a drastic change of weakness from the bonding interface to the aggregate,reducing compressive strength by up to 24.2%and tensile strength by 27.8%.Similarly,the increase in the volume ratio of lightweight aggregate from 25%to 40%leads to a reduction in compressive strength by 13.0%and tensile strength by 9.23%.These results highlight the imperative role of microscale properties on the mechanical properties of LWAC.By supplying precise quantitative details on the effect of porosity and aggregate volume ratio,this research makes significant contributions to construction materials science by providing useful recommendations for the creation and optimization of LWAC with improved performance and sustainability in construction.展开更多
Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propo...Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.展开更多
Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance I...Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance Imaging(MRI)and Computed Tomography(CT)have become essential tools for diagnosing and assessing kidney disorders.However,accurate analysis of thesemedical images is critical for detecting and evaluating tumor severity.This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images.The proposed framework fuses a customized U-Net and Mask R-CNN using a weighted scheme to achieve semantic and instance-level segmentation.The fused outputs are further refined through edge detection using Stochastic FeatureMapping Neural Networks(SFMNN),while volumetric consistency is ensured through Improved Mini-Batch K-Means(IMBKM)clustering integrated with an Encoder-Decoder Convolutional Neural Network(EDCNN).The outputs of these three stages are combined through a weighted fusion mechanism,with optimal weights determined empirically.Experiments on MRI scans from the TCGA-KIRC dataset demonstrate that the proposed hybrid framework significantly outperforms standalone models,achieving a Dice Score of 92.5%,an IoU of 87.8%,a Precision of 93.1%,a Recall of 90.8%,and a Hausdorff Distance of 2.8 mm.These findings validate that the weighted integration of complementary architectures effectively overcomes key limitations in kidney tumor segmentation,leading to improved diagnostic accuracy and robustness in medical image analysis.展开更多
Background knowledge is important for data mining, especially in complicated situation. Ontological engineering is the successor of knowledge engineering. The sharable knowledge bases built on ontology can be used to ...Background knowledge is important for data mining, especially in complicated situation. Ontological engineering is the successor of knowledge engineering. The sharable knowledge bases built on ontology can be used to provide background knowledge to direct the process of data mining. This paper gives a common introduction to the method and presents a practical analysis example using SVM (support vector machine) as the classifier. Gene Ontology and the accompanying annotations compose a big knowledge base, on which many researches have been carried out. Microarray dataset is the output of DNA chip. With the help of Gene Ontology we present a more elaborate analysis on microarray data than former researchers. The method can also be used in other fields with similar scenario.展开更多
Because of their superior problem-solving ability,nature-inspired optimization algorithms are being regularly used in solving complex real-world optimization problems.Engineering academics have recently focused on met...Because of their superior problem-solving ability,nature-inspired optimization algorithms are being regularly used in solving complex real-world optimization problems.Engineering academics have recently focused on meta-heuristic algorithms to solve various optimization challenges.Among the state-of-the-art algorithms,Differential Evolution(DE)is one of the most successful algorithms and is frequently used to solve various industrial problems.Over the previous 2 decades,DE has been heavily modified to improve its capabilities.Several DE variations secured positions in IEEE CEC competitions,establishing their efficacy.However,to our knowledge,there has never been a comparison of performance across various CEC-winning DE versions,which could aid in determining which is the most successful.In this study,the performance of DE and its eight other IEEE CEC competition-winning variants are compared.First,the algorithms have evaluated IEEE CEC 2019 and 2020 bound-constrained functions,and the performances have been compared.One unconstrained problem from IEEE CEC 2011 problem suite and five other constrained mechanical engineering design problems,out of which four issues have been taken from IEEE CEC 2020 non-convex constrained optimization suite,have been solved to compare the performances.Statistical analyses like Friedman's test and Wilcoxon's test are executed to verify the algorithm’s ability statistically.Performance analysis exposes that none of the DE variants can solve all the problems efficiently.Performance of SHADE and ELSHADE-SPACMA are considerable among the methods used for comparison to solve such mechanical design problems.展开更多
This work aims to implement expert and collaborative group recommendation services through an analysis of expertise and network relations NTIS. First of all, expertise database has been constructed by extracting keywo...This work aims to implement expert and collaborative group recommendation services through an analysis of expertise and network relations NTIS. First of all, expertise database has been constructed by extracting keywords after indexing national R＆D information in Korea （human resources, project and outcome） and applying expertise calculation algorithm. In consideration of the characteristics of national R＆D information, weight values have been selected. Then, expertise points were calculated by applying weighted values. In addition, joint research and collaborative relations were implemented in a knowledge map format through network analysis using national R＆D information.展开更多
Technology has grown rapidly with scientific advancement over the world in recent decades. Therefore, there is a need to redesign the medical system to meet their better needs .With the advent of cloud computing the d...Technology has grown rapidly with scientific advancement over the world in recent decades. Therefore, there is a need to redesign the medical system to meet their better needs .With the advent of cloud computing the doctors can keep their information about their critical diseases, critical cases and sophisticated problem they phased .cloud computing has made it possible to solve many complex problems very fast and at a lower cost in less time. Almost all doctors in various disciplines uses cloud computing to solve their problems. In this paper, attention is given to possible implementation of Cloud computing technology in the medical field, especially in hospitals where there is intensive need of computers for better cure for the diseases.展开更多
Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease ...Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.展开更多
Automated segmentation of white matter (WM) and gray matter (GM) is a very important task for detecting multiple diseases. The paper proposed a simple method for WM and GM extraction form magnetic resonance imaging (M...Automated segmentation of white matter (WM) and gray matter (GM) is a very important task for detecting multiple diseases. The paper proposed a simple method for WM and GM extraction form magnetic resonance imaging (MRI) of brain. The proposed methods based on binarization, wavelet decomposition, and convexhull produce very effective results in the context of visual inspection and as well as quantifiably. It tested on three different (Transvers, Sagittal, Coronal) types of MRI of brain image and the validation of experiment indicate accurate detection and segmentation of the interesting structures or particular region of MRI of brain image.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
文摘Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest,storage,preparation,and consumption.This current paper seeks to demystify the importance of artificial intelligence,machine learning(ML),deep learning(DL),and computer vision(CV)in ensuring food safety and quality.By stressing the importance of these technologies,the audience will feel reassured and confident in their potential.These are very handy for such problems,giving assurance over food safety.CV is incredibly noble in today's generation because it improves food processing quality and positively impacts firms and researchers.Thus,at the present production stage,rich in image processing and computer visioning is incorporated into all facets of food production.In this field,DL and ML are implemented to identify the type of food in addition to quality.Concerning data and result-oriented perceptions,one has found similarities regarding various approaches.As a result,the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered.It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further.Also,DL is accurately integrated with identifying the quality and safety of foods in the market.This paper describes the current practices and concerns of ML,DL,and probable trends for its future development.
基金funded and supported by the Ongoing Research Funding program(ORF-2025-314),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.
基金supported by the Competitive Research Fund of the University of Aizu,Japan(Grant No.P-13).
文摘Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart disease,but more remains to be accomplished.The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches.By using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single feature.We processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data quality.Furthermore,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the feature.To reduce the dimensionality,we subsequently used PCA with 95%variation.To identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble models.The model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested approach.This illustrates how interaction-focused feature analysis can produce precise and useful insights for heart disease diagnosis.
文摘Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints,notably the no-cloning theorem,which prohibits the exact duplication of unknown quantum states and has profound implications for cryptography,secure communication,and error correction.While existing quantum circuit representations implicitly honor such constraints,they lack formal mechanisms for early-stage verification in software design.Addressing this constraint at the design phase is essential to ensure the correctness and reliability of quantum software.This paper presents a formal metamodeling framework using UML-style notation and and Object Constraint Language(OCL)to systematically capture and enforce the no-cloning theorem within quantum software models.The proposed metamodel formalizes key quantum concepts—such as entanglement and teleportation—and encodes enforceable invariants that reflect core quantum mechanical laws.The framework’s effectiveness is validated by analyzing two critical edge cases—conditional copying with CNOT gates and quantum teleportation—through instance model evaluations.These cases demonstrate that the metamodel can capture nuanced scenarios that are often mistaken as violations of the no-cloning theorem but are proven compliant under formal analysis.Thus,these serve as constructive validations that demonstrate the metamodel’s expressiveness and correctness in representing operations that may appear to challenge the no-cloning theorem but,upon rigorous analysis,are shown to comply with it.The approach supports early detection of conceptual design errors,promoting correctness prior to implementation.The framework’s extensibility is also demonstrated by modeling projective measurement,further reinforcing its applicability to broader quantum software engineering tasks.By integrating the rigor of metamodeling with fundamental quantum mechanical principles,this work provides a structured,model-driven approach that enables traditional software engineers to address quantum computing challenges.It offers practical insights into embedding quantum correctness at the modeling level and advances the development of reliable,error-resilient quantum software systems.
基金The National Science Foundation funded this research under the Dy-namics of Coupled Natural and Human Systems program(Grants No.DEB-1212183 and BCS-1826839)support from San Diego State University and Auburn University.
文摘A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.
文摘The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.
文摘Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.
基金the Begum Rokeya University,Rangpur,and the United Arab Emirates University,UAE for partially supporting this work。
文摘Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates disease severity,supports effective treatment strategies,and reduces reliance on excessive pesticide use.Traditional machine learning approaches have been applied for automated rice disease diagnosis;however,these methods depend heavily on manual image preprocessing and handcrafted feature extraction,which are labor-intensive and time-consuming and often require domain expertise.Recently,end-to-end deep learning(DL) models have been introduced for this task,but they often lack robustness and generalizability across diverse datasets.To address these limitations,we propose a novel end-toend training framework for convolutional neural network(CNN) and attention-based model ensembles(E2ETCA).This framework integrates features from two state-of-the-art(SOTA) CNN models,Inception V3 and DenseNet-201,and an attention-based vision transformer(ViT) model.The fused features are passed through an additional fully connected layer with softmax activation for final classification.The entire process is trained end-to-end,enhancing its suitability for realworld deployment.Furthermore,we extract and analyze the learned features using a support vector machine(SVM),a traditional machine learning classifier,to provide comparative insights.We evaluate the proposed E2ETCA framework on three publicly available datasets,the Mendeley Rice Leaf Disease Image Samples dataset,the Kaggle Rice Diseases Image dataset,the Bangladesh Rice Research Institute dataset,and a combined version of all three.Using standard evaluation metrics(accuracy,precision,recall,and F1-score),our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis,with potential applicability to other agricultural disease detection tasks.
文摘The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.
基金supported by the Innovative Human Resource Development for Local Intel-lectualization program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.IITP-2026-2020-0-01741)the research fund of Hanyang University(HY-2025-1110).
文摘Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.
基金supported by National Science Foundation of China(10972015,11172015)the Beijing Natural Science Foundation(8162008).
文摘In modern construction,Lightweight Aggregate Concrete(LWAC)has been recognized as a vital material of concern because of its unique properties,such as reduced density and improved thermal insulation.Despite the extensive knowledge regarding its macroscopic properties,there is a wide knowledge gap in understanding the influence of microscale parameters like aggregate porosity and volume ratio on the mechanical response of LWAC.This study aims to bridge this knowledge gap,spurred by the need to enhance the predictability and applicability of LWAC in various construction environments.With the help of advanced numerical methods,including the finite element method and a random circular aggregate model,this study critically evaluates the role played by these microscale factors.We found that an increase in the aggregate porosity from 23.5%to 48.5%leads to a drastic change of weakness from the bonding interface to the aggregate,reducing compressive strength by up to 24.2%and tensile strength by 27.8%.Similarly,the increase in the volume ratio of lightweight aggregate from 25%to 40%leads to a reduction in compressive strength by 13.0%and tensile strength by 9.23%.These results highlight the imperative role of microscale properties on the mechanical properties of LWAC.By supplying precise quantitative details on the effect of porosity and aggregate volume ratio,this research makes significant contributions to construction materials science by providing useful recommendations for the creation and optimization of LWAC with improved performance and sustainability in construction.
基金funded by Multimedia University,Cyberjaya,Selangor,Malaysia(Grant Number:PostDoc(MMUI/240029)).
文摘Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.
基金funded by the Ongoing Research Funding Program-Research Chairs(ORF-RC-2025-2400),King Saud University,Riyadh,Saudi Arabia。
文摘Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance Imaging(MRI)and Computed Tomography(CT)have become essential tools for diagnosing and assessing kidney disorders.However,accurate analysis of thesemedical images is critical for detecting and evaluating tumor severity.This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images.The proposed framework fuses a customized U-Net and Mask R-CNN using a weighted scheme to achieve semantic and instance-level segmentation.The fused outputs are further refined through edge detection using Stochastic FeatureMapping Neural Networks(SFMNN),while volumetric consistency is ensured through Improved Mini-Batch K-Means(IMBKM)clustering integrated with an Encoder-Decoder Convolutional Neural Network(EDCNN).The outputs of these three stages are combined through a weighted fusion mechanism,with optimal weights determined empirically.Experiments on MRI scans from the TCGA-KIRC dataset demonstrate that the proposed hybrid framework significantly outperforms standalone models,achieving a Dice Score of 92.5%,an IoU of 87.8%,a Precision of 93.1%,a Recall of 90.8%,and a Hausdorff Distance of 2.8 mm.These findings validate that the weighted integration of complementary architectures effectively overcomes key limitations in kidney tumor segmentation,leading to improved diagnostic accuracy and robustness in medical image analysis.
基金Project (No. 20040248001) supported by the Ph.D. Programs Foun-dation of Ministry of Education of China
文摘Background knowledge is important for data mining, especially in complicated situation. Ontological engineering is the successor of knowledge engineering. The sharable knowledge bases built on ontology can be used to provide background knowledge to direct the process of data mining. This paper gives a common introduction to the method and presents a practical analysis example using SVM (support vector machine) as the classifier. Gene Ontology and the accompanying annotations compose a big knowledge base, on which many researches have been carried out. Microarray dataset is the output of DNA chip. With the help of Gene Ontology we present a more elaborate analysis on microarray data than former researchers. The method can also be used in other fields with similar scenario.
文摘Because of their superior problem-solving ability,nature-inspired optimization algorithms are being regularly used in solving complex real-world optimization problems.Engineering academics have recently focused on meta-heuristic algorithms to solve various optimization challenges.Among the state-of-the-art algorithms,Differential Evolution(DE)is one of the most successful algorithms and is frequently used to solve various industrial problems.Over the previous 2 decades,DE has been heavily modified to improve its capabilities.Several DE variations secured positions in IEEE CEC competitions,establishing their efficacy.However,to our knowledge,there has never been a comparison of performance across various CEC-winning DE versions,which could aid in determining which is the most successful.In this study,the performance of DE and its eight other IEEE CEC competition-winning variants are compared.First,the algorithms have evaluated IEEE CEC 2019 and 2020 bound-constrained functions,and the performances have been compared.One unconstrained problem from IEEE CEC 2011 problem suite and five other constrained mechanical engineering design problems,out of which four issues have been taken from IEEE CEC 2020 non-convex constrained optimization suite,have been solved to compare the performances.Statistical analyses like Friedman's test and Wilcoxon's test are executed to verify the algorithm’s ability statistically.Performance analysis exposes that none of the DE variants can solve all the problems efficiently.Performance of SHADE and ELSHADE-SPACMA are considerable among the methods used for comparison to solve such mechanical design problems.
基金Project(N-12-NM-LU01-C01) supported by Construction of NTIS (National Science & Technology Information Service) Program Funded by the National Science & Technology Commission (NSTC), Korea
文摘This work aims to implement expert and collaborative group recommendation services through an analysis of expertise and network relations NTIS. First of all, expertise database has been constructed by extracting keywords after indexing national R＆D information in Korea （human resources, project and outcome） and applying expertise calculation algorithm. In consideration of the characteristics of national R＆D information, weight values have been selected. Then, expertise points were calculated by applying weighted values. In addition, joint research and collaborative relations were implemented in a knowledge map format through network analysis using national R＆D information.
文摘Technology has grown rapidly with scientific advancement over the world in recent decades. Therefore, there is a need to redesign the medical system to meet their better needs .With the advent of cloud computing the doctors can keep their information about their critical diseases, critical cases and sophisticated problem they phased .cloud computing has made it possible to solve many complex problems very fast and at a lower cost in less time. Almost all doctors in various disciplines uses cloud computing to solve their problems. In this paper, attention is given to possible implementation of Cloud computing technology in the medical field, especially in hospitals where there is intensive need of computers for better cure for the diseases.
基金support from the Deanship for Research&Innovation,Ministry of Education in Saudi Arabia,under the Auspices of Project Number:IFP22UQU4281768DSR122.
文摘Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.
文摘Automated segmentation of white matter (WM) and gray matter (GM) is a very important task for detecting multiple diseases. The paper proposed a simple method for WM and GM extraction form magnetic resonance imaging (MRI) of brain. The proposed methods based on binarization, wavelet decomposition, and convexhull produce very effective results in the context of visual inspection and as well as quantifiably. It tested on three different (Transvers, Sagittal, Coronal) types of MRI of brain image and the validation of experiment indicate accurate detection and segmentation of the interesting structures or particular region of MRI of brain image.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
