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
Blockchain Technology(BT)has emerged as a transformative solution for improving the efficacy,security,and transparency of supply chain intelligence.Traditional Supply Chain Management(SCM)systems frequently have probl...Blockchain Technology(BT)has emerged as a transformative solution for improving the efficacy,security,and transparency of supply chain intelligence.Traditional Supply Chain Management(SCM)systems frequently have problems such as data silos,a lack of visibility in real time,fraudulent activities,and inefficiencies in tracking and traceability.Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues;it facilitates trust,security,and the sharing of data in real-time among all parties involved.Through an examination of critical technologies,methodology,and applications,this paper delves deeply into computer modeling based-blockchain framework within supply chain intelligence.The effect of BT on SCM is evaluated by reviewing current research and practical applications in the field.As part of the process,we delved through the research on blockchain-based supply chain models,smart contracts,Decentralized Applications(DApps),and how they connect to other cutting-edge innovations like Artificial Intelligence(AI)and the Internet of Things(IoT).To quantify blockchain’s performance,the study introduces analytical models for efficiency improvement,security enhancement,and scalability,enabling computational assessment and simulation of supply chain scenarios.These models provide a structured approach to predicting system performance under varying parameters.According to the results,BT increases efficiency by automating transactions using smart contracts,increases security by using cryptographic techniques,and improves transparency in the supply chain by providing immutable records.Regulatory concerns,challenges with interoperability,and scalability all work against broad adoption.To fully automate and intelligently integrate blockchain with AI and the IoT,additional research is needed to address blockchain’s current limitations and realize its potential for supply chain intelligence.展开更多
Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(D...Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive analytics.CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains.This survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation capabilities.The layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless integration.The study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these obstacles.Applications in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT integration.In addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT systems.By synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and practitioners.The findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.展开更多
This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images.The proposed approach employs the Attention U-Net architecture,enhance...This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images.The proposed approach employs the Attention U-Net architecture,enhanced with gated attention mechanisms,to refine segmentation by emphasizing spatially relevant anatomical features while suppressing irrelevant details.By isolating skeletal structures which remain stable over time compared to soft tissues,this method leverages bones as reliable biometric markers for identity verification.The model integrates custom-designed encoder and decoder blocks with attention gates,achieving high segmentation precision.To evaluate the impact of architectural choices,we conducted an ablation study comparing Attention U-Net with and without attentionmechanisms,alongside an analysis of data augmentation effects.Training and evaluation were performed on a curated chest X-ray dataset,with segmentation performance measured using Dice score,precision,and loss functions,achieving over 98% precision and 94% Dice score.The extracted bone structures were further processed to derive unique biometric patterns,enabling robust and privacy-preserving person identification.Our findings highlight the effectiveness of attentionmechanisms in improving segmentation accuracy and underscore the potential of chest bonebased biometrics in forensic and medical imaging.This work paves the way for integrating artificial intelligence into real-world forensic workflows,offering a non-invasive and reliable solution for post-mortem identification.展开更多
Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions,and it has many types,from normal to serious.Hepatitis is diagnosed through many blood tests and factors;Artificial Int...Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions,and it has many types,from normal to serious.Hepatitis is diagnosed through many blood tests and factors;Artificial Intelligence(AI)techniques have played an important role in early diagnosis and help physicians make decisions.This study evaluated the performance of Machine Learning(ML)algorithms on the hepatitis data set.The dataset contains missing values that have been processed and outliers removed.The dataset was counterbalanced by the Synthetic Minority Over-sampling Technique(SMOTE).The features of the data set were processed in two ways:first,the application of the Recursive Feature Elimination(RFE)algorithm to arrange the percentage of contribution of each feature to the diagnosis of hepatitis,then selection of important features using the t-distributed Stochastic Neighbor Embedding(t-SNE)and Principal Component Analysis(PCA)algorithms.Second,the SelectKBest function was applied to give scores for each attribute,followed by the t-SNE and PCA algorithms.Finally,the classification algorithms K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Artificial Neural Network(ANN),Decision Tree(DT),and Random Forest(RF)were fed by the dataset after processing the features in different methods are RFE with t-SNE and PCA and SelectKBest with t-SNE and PCA).All algorithms yielded promising results for diagnosing hepatitis data sets.The RF with RFE and PCA methods achieved accuracy,Precision,Recall,and AUC of 97.18%,96.72%,97.29%,and 94.2%,respectively,during the training phase.During the testing phase,it reached accuracy,Precision,Recall,and AUC by 96.31%,95.23%,97.11%,and 92.67%,respectively.展开更多
This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data character...This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness,heavy tails,and diverse hazard behaviors.We meticulously develop the TIHTBXII’s mathematical foundations,including its probability density function(PDF),cumulative distribution function(CDF),and essential statistical properties,crucial for theoretical understanding and practical application.A comprehensive Monte Carlo simulation evaluates four parameter estimation methods:maximum likelihood(MLE),maximum product spacing(MPS),least squares(LS),and weighted least squares(WLS).The simulation results consistently show that as sample sizes increase,the Bias and RMSE of all estimators decrease,with WLS and LS often demonstrating superior and more stable performance.Beyond theoretical development,we present a practical application of the TIHTBXII distribution in constructing a group acceptance sampling plan(GASP)for truncated life tests.This application highlights how the TIHTBXII model can optimize quality control decisions by minimizing the average sample number(ASN)while effectively managing consumer and producer risks.Empirical validation using real-world datasets,including“Active Repair Duration,”“Groundwater Contaminant Measurements,”and“Dominica COVID-19 Mortality,”further demonstrates the TIHTBXII’s superior fit compared to existing models.Our findings confirm the TIHTBXII distribution as a powerful and reliable alternative for accurately modeling complex data in fields such as reliability engineering and quality assessment,leading to more informed and robust decision-making.展开更多
The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and stron...The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and strong global search capabilities,this algorithm finds application across diverse optimization problem domains.However,in the face of increasingly complex optimization challenges,the Bat algorithm encounters certain limitations,such as slow convergence and sensitivity to initial solutions.In order to tackle these challenges,the present study incorporates a range of optimization compo-nents into the Bat algorithm,thereby proposing a variant called PKEBA.A projection screening strategy is implemented to mitigate its sensitivity to initial solutions,thereby enhancing the quality of the initial solution set.A kinetic adaptation strategy reforms exploration patterns,while an elite communication strategy enhances group interaction,to avoid algorithm from local optima.Subsequently,the effectiveness of the proposed PKEBA is rigorously evaluated.Testing encompasses 30 benchmark functions from IEEE CEC2014,featuring ablation experiments and comparative assessments against classical algorithms and their variants.Moreover,real-world engineering problems are employed as further validation.The results conclusively demonstrate that PKEBA ex-hibits superior convergence and precision compared to existing algorithms.展开更多
This research investigates the application of digital images in military contexts by utilizing analytical equations to augment human visual capabilities.A comparable filter is used to improve the visual quality of the...This research investigates the application of digital images in military contexts by utilizing analytical equations to augment human visual capabilities.A comparable filter is used to improve the visual quality of the photographs by reducing truncations in the existing images.Furthermore,the collected images undergo processing using histogram gradients and a flexible threshold value that may be adjusted in specific situations.Thus,it is possible to reduce the occurrence of overlapping circumstances in collective picture characteristics by substituting grey-scale photos with colorized factors.The proposed method offers additional robust feature representations by imposing a limiting factor to reduce overall scattering values.This is achieved by visualizing a graphical function.Moreover,to derive valuable insights from a series of photos,both the separation and in-version processes are conducted.This involves analyzing comparison results across four different scenarios.The results of the comparative analysis show that the proposed method effectively reduces the difficulties associated with time and space to 1 s and 3%,respectively.In contrast,the existing strategy exhibits higher complexities of 3 s and 9.1%,respectively.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
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
A novel broad concept of numbers was given out based upon the analyzing of numbers′ carrying rule. In international mathematics & computer science, all researches on numbers are only confined to same varying rule...A novel broad concept of numbers was given out based upon the analyzing of numbers′ carrying rule. In international mathematics & computer science, all researches on numbers are only confined to same varying rule of the FCN (fixed carrying numbers). The concept of VCN (variable carrying numbers) was presented, and some applied examples of practice were given out. So the engineering application of VCN for n-figures is wider than that of FCN in human society.展开更多
This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications.As a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the matin...This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications.As a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the mating characteristics of snakes to find the optimal solution.SO has a simple structure and offers a delicate balance between exploitation and exploration.However,it also has some shortcomings to be improved.The proposed BEESO consequently aims to lighten the issues of lack of population diversity,convergence slowness,and the tendency to be stuck in local optima in SO.The presentation of Bi-Directional Search(BDS)is to approach the global optimal value along the direction guided by the best and the worst individuals,which makes the convergence speed faster.The increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics(MEPD),and the replacement of poorer quality individuals improves population quality.The Elite Opposition-Based Learning(EOBL)provides improved local exploitation ability of BEESO by utilizing solid solutions with good performance.The performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering designs.Additionally,the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum tests.The findings show that these introduced strategies provide some improvements in the performance of SO,and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all algorithms.To conclude,the proposed BEESO offers a good alternative to solving optimization issues.展开更多
Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital ...Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.展开更多
Depression is a serious medical condition and is a leading cause of disability worldwide.Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations,lack of...Depression is a serious medical condition and is a leading cause of disability worldwide.Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations,lack of objective assessments,and assessments that rely on patients'perceptions,memory,and recall.Digital phenotyping(DP),especially assessments conducted using mobile health technologies,has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes.DP includes two primary sources of digital data generated using ecological momentary assessments(EMA),assessments conducted in real-time,in subjects'natural environment.This includes active EMA,data that require active input by the subject,and passive EMA or passive sensing,data passively and automatically collected from subjects'personal digital devices.The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients'clinical status.Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status.These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients.Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines.The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations.A clinically-relevant model for incorporating DP in clinical setting is presented.This model,based on investigations conducted by our group,delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process.Benefits,challenges,and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.展开更多
In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM1...In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM10000,ISBI2018,and ISBI2019 datasets.Initially,we consider a pretrained deep neural network model,DarkeNet19,and fine-tune the parameters of third convolutional layer to generate the image gradients.All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network(HFaFFNN).The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image.Later,two pre-trained deep models,Darknet-53 and NasNet-mobile,are employed and fine-tuned according to the selected datasets.The concept of transfer learning is later explored to train both models,where the input feed is the generated localized lesion images.In the subsequent step,the extracted features are fused using parallel max entropy correlation(PMEC)technique.To avoid the problem of overfitting and to select the most discriminant feature information,we implement a hybrid optimization algorithm called entropy-kurtosis controlled whale optimization(EKWO)algorithm.The selected features are finally passed to the softmax classifier for the final classification.Three datasets are used for the experimental process,such as HAM10000,ISBI2018,and ISBI2019 to achieve an accuracy of 95.8%,97.1%,and 85.35%,respectively.展开更多
In the system of Computer Network Collaborative Defense(CNCD),it is difficult to evaluate the trustworthiness of defense agents which are newly added to the system,since they lack historical interaction for trust eval...In the system of Computer Network Collaborative Defense(CNCD),it is difficult to evaluate the trustworthiness of defense agents which are newly added to the system,since they lack historical interaction for trust evaluation.This will lead that the newly added agents could not get reasonable initial trustworthiness,and affect the whole process of trust evaluation.To solve this problem in CNCD,a trust type based trust bootstrapping model was introduced in this research.First,the division of trust type,trust utility and defense cost were discussed.Then the constraints of defense tasks were analyzed based on game theory.According to the constraints obtained,the trust type of defense agents was identified and the initial trustworthiness was assigned to defense agents.The simulated experiment shows that the methods proposed have lower failure rate of defense tasks and better adaptability in the respect of defense task execution.展开更多
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supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Blockchain Technology(BT)has emerged as a transformative solution for improving the efficacy,security,and transparency of supply chain intelligence.Traditional Supply Chain Management(SCM)systems frequently have problems such as data silos,a lack of visibility in real time,fraudulent activities,and inefficiencies in tracking and traceability.Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues;it facilitates trust,security,and the sharing of data in real-time among all parties involved.Through an examination of critical technologies,methodology,and applications,this paper delves deeply into computer modeling based-blockchain framework within supply chain intelligence.The effect of BT on SCM is evaluated by reviewing current research and practical applications in the field.As part of the process,we delved through the research on blockchain-based supply chain models,smart contracts,Decentralized Applications(DApps),and how they connect to other cutting-edge innovations like Artificial Intelligence(AI)and the Internet of Things(IoT).To quantify blockchain’s performance,the study introduces analytical models for efficiency improvement,security enhancement,and scalability,enabling computational assessment and simulation of supply chain scenarios.These models provide a structured approach to predicting system performance under varying parameters.According to the results,BT increases efficiency by automating transactions using smart contracts,increases security by using cryptographic techniques,and improves transparency in the supply chain by providing immutable records.Regulatory concerns,challenges with interoperability,and scalability all work against broad adoption.To fully automate and intelligently integrate blockchain with AI and the IoT,additional research is needed to address blockchain’s current limitations and realize its potential for supply chain intelligence.
文摘Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive analytics.CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains.This survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation capabilities.The layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless integration.The study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these obstacles.Applications in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT integration.In addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT systems.By synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and practitioners.The findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.
基金funded by Umm Al-Qura University,Saudi Arabia under grant number:25UQU4300346GSSR08.
文摘This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images.The proposed approach employs the Attention U-Net architecture,enhanced with gated attention mechanisms,to refine segmentation by emphasizing spatially relevant anatomical features while suppressing irrelevant details.By isolating skeletal structures which remain stable over time compared to soft tissues,this method leverages bones as reliable biometric markers for identity verification.The model integrates custom-designed encoder and decoder blocks with attention gates,achieving high segmentation precision.To evaluate the impact of architectural choices,we conducted an ablation study comparing Attention U-Net with and without attentionmechanisms,alongside an analysis of data augmentation effects.Training and evaluation were performed on a curated chest X-ray dataset,with segmentation performance measured using Dice score,precision,and loss functions,achieving over 98% precision and 94% Dice score.The extracted bone structures were further processed to derive unique biometric patterns,enabling robust and privacy-preserving person identification.Our findings highlight the effectiveness of attentionmechanisms in improving segmentation accuracy and underscore the potential of chest bonebased biometrics in forensic and medical imaging.This work paves the way for integrating artificial intelligence into real-world forensic workflows,offering a non-invasive and reliable solution for post-mortem identification.
基金funded by Scientific Research Deanship at University of Ha’il,Saudi Arabia,through project number GR-24009.
文摘Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions,and it has many types,from normal to serious.Hepatitis is diagnosed through many blood tests and factors;Artificial Intelligence(AI)techniques have played an important role in early diagnosis and help physicians make decisions.This study evaluated the performance of Machine Learning(ML)algorithms on the hepatitis data set.The dataset contains missing values that have been processed and outliers removed.The dataset was counterbalanced by the Synthetic Minority Over-sampling Technique(SMOTE).The features of the data set were processed in two ways:first,the application of the Recursive Feature Elimination(RFE)algorithm to arrange the percentage of contribution of each feature to the diagnosis of hepatitis,then selection of important features using the t-distributed Stochastic Neighbor Embedding(t-SNE)and Principal Component Analysis(PCA)algorithms.Second,the SelectKBest function was applied to give scores for each attribute,followed by the t-SNE and PCA algorithms.Finally,the classification algorithms K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Artificial Neural Network(ANN),Decision Tree(DT),and Random Forest(RF)were fed by the dataset after processing the features in different methods are RFE with t-SNE and PCA and SelectKBest with t-SNE and PCA).All algorithms yielded promising results for diagnosing hepatitis data sets.The RF with RFE and PCA methods achieved accuracy,Precision,Recall,and AUC of 97.18%,96.72%,97.29%,and 94.2%,respectively,during the training phase.During the testing phase,it reached accuracy,Precision,Recall,and AUC by 96.31%,95.23%,97.11%,and 92.67%,respectively.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(Grant Number IMSIU-DDRSP2501).
文摘This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness,heavy tails,and diverse hazard behaviors.We meticulously develop the TIHTBXII’s mathematical foundations,including its probability density function(PDF),cumulative distribution function(CDF),and essential statistical properties,crucial for theoretical understanding and practical application.A comprehensive Monte Carlo simulation evaluates four parameter estimation methods:maximum likelihood(MLE),maximum product spacing(MPS),least squares(LS),and weighted least squares(WLS).The simulation results consistently show that as sample sizes increase,the Bias and RMSE of all estimators decrease,with WLS and LS often demonstrating superior and more stable performance.Beyond theoretical development,we present a practical application of the TIHTBXII distribution in constructing a group acceptance sampling plan(GASP)for truncated life tests.This application highlights how the TIHTBXII model can optimize quality control decisions by minimizing the average sample number(ASN)while effectively managing consumer and producer risks.Empirical validation using real-world datasets,including“Active Repair Duration,”“Groundwater Contaminant Measurements,”and“Dominica COVID-19 Mortality,”further demonstrates the TIHTBXII’s superior fit compared to existing models.Our findings confirm the TIHTBXII distribution as a powerful and reliable alternative for accurately modeling complex data in fields such as reliability engineering and quality assessment,leading to more informed and robust decision-making.
基金partially supported by MRC(MC_PC_17171)Royal Society(RP202G0230)+8 种基金BHF(AA/18/3/34220)Hope Foundation for Cancer Research(RM60G0680)GCRF(20P2PF11)Sino-UK Industrial Fund(RP202G0289)LIAS(20P2ED10,20P2RE969)Data Science Enhancement Fund(20P2RE237)Fight for Sight(24NN201)Sino-UK Education Fund(OP202006)BBSRC(RM32G0178B8).
文摘The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and strong global search capabilities,this algorithm finds application across diverse optimization problem domains.However,in the face of increasingly complex optimization challenges,the Bat algorithm encounters certain limitations,such as slow convergence and sensitivity to initial solutions.In order to tackle these challenges,the present study incorporates a range of optimization compo-nents into the Bat algorithm,thereby proposing a variant called PKEBA.A projection screening strategy is implemented to mitigate its sensitivity to initial solutions,thereby enhancing the quality of the initial solution set.A kinetic adaptation strategy reforms exploration patterns,while an elite communication strategy enhances group interaction,to avoid algorithm from local optima.Subsequently,the effectiveness of the proposed PKEBA is rigorously evaluated.Testing encompasses 30 benchmark functions from IEEE CEC2014,featuring ablation experiments and comparative assessments against classical algorithms and their variants.Moreover,real-world engineering problems are employed as further validation.The results conclusively demonstrate that PKEBA ex-hibits superior convergence and precision compared to existing algorithms.
基金financially supported by Ongoing Research Funding Program(ORF-2025-846),King Saud University,Riyadh,Saudi Arabia.
文摘This research investigates the application of digital images in military contexts by utilizing analytical equations to augment human visual capabilities.A comparable filter is used to improve the visual quality of the photographs by reducing truncations in the existing images.Furthermore,the collected images undergo processing using histogram gradients and a flexible threshold value that may be adjusted in specific situations.Thus,it is possible to reduce the occurrence of overlapping circumstances in collective picture characteristics by substituting grey-scale photos with colorized factors.The proposed method offers additional robust feature representations by imposing a limiting factor to reduce overall scattering values.This is achieved by visualizing a graphical function.Moreover,to derive valuable insights from a series of photos,both the separation and in-version processes are conducted.This involves analyzing comparison results across four different scenarios.The results of the comparative analysis show that the proposed method effectively reduces the difficulties associated with time and space to 1 s and 3%,respectively.In contrast,the existing strategy exhibits higher complexities of 3 s and 9.1%,respectively.
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.
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.
基金This research is supported by the Nature Science Foundation of Fujian Province of China(item No.2006J0414foundation No.A0640015)
文摘A novel broad concept of numbers was given out based upon the analyzing of numbers′ carrying rule. In international mathematics & computer science, all researches on numbers are only confined to same varying rule of the FCN (fixed carrying numbers). The concept of VCN (variable carrying numbers) was presented, and some applied examples of practice were given out. So the engineering application of VCN for n-figures is wider than that of FCN in human society.
基金supported by the National Natural Science Foundation of China (Grant No.51875454).
文摘This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications.As a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the mating characteristics of snakes to find the optimal solution.SO has a simple structure and offers a delicate balance between exploitation and exploration.However,it also has some shortcomings to be improved.The proposed BEESO consequently aims to lighten the issues of lack of population diversity,convergence slowness,and the tendency to be stuck in local optima in SO.The presentation of Bi-Directional Search(BDS)is to approach the global optimal value along the direction guided by the best and the worst individuals,which makes the convergence speed faster.The increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics(MEPD),and the replacement of poorer quality individuals improves population quality.The Elite Opposition-Based Learning(EOBL)provides improved local exploitation ability of BEESO by utilizing solid solutions with good performance.The performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering designs.Additionally,the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum tests.The findings show that these introduced strategies provide some improvements in the performance of SO,and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all algorithms.To conclude,the proposed BEESO offers a good alternative to solving optimization issues.
文摘Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.
文摘Depression is a serious medical condition and is a leading cause of disability worldwide.Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations,lack of objective assessments,and assessments that rely on patients'perceptions,memory,and recall.Digital phenotyping(DP),especially assessments conducted using mobile health technologies,has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes.DP includes two primary sources of digital data generated using ecological momentary assessments(EMA),assessments conducted in real-time,in subjects'natural environment.This includes active EMA,data that require active input by the subject,and passive EMA or passive sensing,data passively and automatically collected from subjects'personal digital devices.The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients'clinical status.Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status.These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients.Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines.The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations.A clinically-relevant model for incorporating DP in clinical setting is presented.This model,based on investigations conducted by our group,delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process.Benefits,challenges,and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)the Soonchunhyang University Research Fund.
文摘In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM10000,ISBI2018,and ISBI2019 datasets.Initially,we consider a pretrained deep neural network model,DarkeNet19,and fine-tune the parameters of third convolutional layer to generate the image gradients.All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network(HFaFFNN).The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image.Later,two pre-trained deep models,Darknet-53 and NasNet-mobile,are employed and fine-tuned according to the selected datasets.The concept of transfer learning is later explored to train both models,where the input feed is the generated localized lesion images.In the subsequent step,the extracted features are fused using parallel max entropy correlation(PMEC)technique.To avoid the problem of overfitting and to select the most discriminant feature information,we implement a hybrid optimization algorithm called entropy-kurtosis controlled whale optimization(EKWO)algorithm.The selected features are finally passed to the softmax classifier for the final classification.Three datasets are used for the experimental process,such as HAM10000,ISBI2018,and ISBI2019 to achieve an accuracy of 95.8%,97.1%,and 85.35%,respectively.
基金supported by the National Natural Science Foundation of China under Grant No.61170295
文摘In the system of Computer Network Collaborative Defense(CNCD),it is difficult to evaluate the trustworthiness of defense agents which are newly added to the system,since they lack historical interaction for trust evaluation.This will lead that the newly added agents could not get reasonable initial trustworthiness,and affect the whole process of trust evaluation.To solve this problem in CNCD,a trust type based trust bootstrapping model was introduced in this research.First,the division of trust type,trust utility and defense cost were discussed.Then the constraints of defense tasks were analyzed based on game theory.According to the constraints obtained,the trust type of defense agents was identified and the initial trustworthiness was assigned to defense agents.The simulated experiment shows that the methods proposed have lower failure rate of defense tasks and better adaptability in the respect of defense task execution.
