In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading...In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.展开更多
In recent years,cyber threats have escalated across diverse sectors,with cybercrime syndicates increasingly exploiting system vulnerabilities.Traditional passive defense mechanisms have proven insufficient,particularl...In recent years,cyber threats have escalated across diverse sectors,with cybercrime syndicates increasingly exploiting system vulnerabilities.Traditional passive defense mechanisms have proven insufficient,particularly as Linux platforms—historically overlooked in favor of Windows—have emerged as frequent targets.According to Trend Micro,there has been a substantial increase in Linux-targeted malware,with ransomware attacks on Linux surpassing those on macOS.This alarming trend underscores the need for detection strategies specifically designed for Linux environments.To address this challenge,this study proposes a comprehensive malware detection framework tailored for Linux systems,integrating dynamic behavioral analysis with the semantic reasoning capabilities of large language models(LLMs).Malware samples are executed within sandbox environments to extract behavioral features such as system calls and command-line executions.These features are then systematically mapped to the MITRE ATT&CK framework,incorporating its defined data sources,data components,and Tactics,Techniques,and Procedures(TTPs).Two mapping constructs—Conceptual Definition Mapping and TTP Technical Keyword Mapping—are developed from official MITRE documentation.These resources are utilized to fine-tune an LLM,enabling it to semantically interpret complex behavioral patterns and infer associated attack techniques,including those employed by previously unknown malware variants.The resulting detection pipeline effectively bridges raw behavioral data with structured threat intelligence.Experimental evaluations confirm the efficacy of the proposed system,with the fine-tuned Gemma 2B model demonstrating significantly enhanced accuracy in associating behavioral features with ATT&CK-defined techniques.This study contributes a fully integrated Linux-specific detection framework,a novel approach for transforming unstructured behavioral data into actionable intelligence,improved interpretability of malicious behavior,and a scalable training process for future applications of LLMs in cybersecurity.展开更多
As is known,centralized federated learning faces risks of a single point of failure and privacy breaches,and blockchain-based federated learning frameworks can address these challenges to a certain extent in recent wo...As is known,centralized federated learning faces risks of a single point of failure and privacy breaches,and blockchain-based federated learning frameworks can address these challenges to a certain extent in recent works.However,malicious clients may still illegally access the blockchain to upload malicious data or steal on-chain data.In addition,blockchain-based federated training suffers from a heavy storage burden and excessive network communication overhead.To address these issues,we propose an asynchronous,tiered federated learning storage scheme based on blockchain and IPFS.It manages the execution of federated learning tasks through smart contracts deployed on the blockchain,decentralizing the entire training process.Additionally,the scheme employs a secure and efficient blockchain-based asynchronous tiered architecture,integrating attribute-based access control technology for resource exchange between the clients and the blockchain network.It dynamically manages access control policies during training and adopts a hybrid data storage strategy combining blockchain and IPFS.Experiments with multiple sets of image classification tasks are conducted,indicating that the storage strategy used in this scheme saves nearly 50 percent of the communication overhead and significantly reduces the on-chain storage burden compared to the traditional blockchain-only storage strategy.In terms of training effectiveness,it maintains similar accuracy as centralized training and minimizes the probability of being attacked.展开更多
To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installa...To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.展开更多
As smart manufacturing and Industry 4.0 continue to evolve,fault diagnosis of mechanical equipment has become crucial for ensuring production safety and optimizing equipment utilization.To address the challenge of cro...As smart manufacturing and Industry 4.0 continue to evolve,fault diagnosis of mechanical equipment has become crucial for ensuring production safety and optimizing equipment utilization.To address the challenge of cross-domain adaptation in intelligent diagnostic models under varying operational conditions,this paper introduces the CNN-1D-KAN model,which combines a 1D Convolutional Neural Network(1D-CNN)with a Kolmogorov–Arnold Network(KAN).The novelty of this approach lies in replacing the traditional 1D-CNN’s final fully connected layer with a KANLinear layer,leveraging KAN’s advanced nonlinear processing and function approximation capabilities while maintaining the simplicity of linear transformations.Experimental results on the CWRU dataset demonstrate that,under stable load conditions,the CNN-1D-KAN model achieves high accuracy,averaging 96.67%.Furthermore,the model exhibits strong transfer generalization and robustness across varying load conditions,sustaining an average accuracy of 90.21%.When compared to traditional neural networks(e.g.,1D-CNN and Multi-Layer Perceptron)and other domain adaptation models(e.g.,KAN Convolutions and KAN),the CNN-1D-KAN consistently outperforms in both accuracy and F1 scores across diverse load scenarios.Particularly in handling complex cross-domain data,it excels in diagnostic performance.This study provides an effective solution for cross-domain fault diagnosis in Industrial Internet systems,offering a theoretical foundation to enhance the reliability and stability of intelligent manufacturing processes,thus supporting the future advancement of industrial IoT applications.展开更多
The compact torus injector(CTI)for the central fueling of the EAST tokamak has undergone significant upgrades to enhance its injection capability.During the initial phase of the platform testing phase,EAST-CTI demonst...The compact torus injector(CTI)for the central fueling of the EAST tokamak has undergone significant upgrades to enhance its injection capability.During the initial phase of the platform testing phase,EAST-CTI demonstrated relatively low performance,with a maximum velocity of 150 km s^(−1) and a single compact torus(CT)plasma mass of 90μg[Kong D et al 2023 Plasma Sci.Technol.25065601].These parameters were insufficient for conducting central fueling experiments on the EAST tokamak.Consequently,extensive upgrades were carried out to improve the performance of the EAST-CTI system.The compression region was extended from 280 mm to 700 mm to prevent rapid compression and deceleration of the CT plasma,along with an extension of the acceleration region to further increase the plasma acceleration.The power supply system has also been upgraded.These improvements elevated the operating voltage from 8 kV to 15 kV,increased the discharge current from 120 kA to 300 kA and enabled repetitive operation at a maximum rate of 2 Hz.As a result,significant advances in EAST-CTI performance were achieved,with the maximum velocity increasing to 330 km s^(−1) and the CT plasma density reaching 1.5×10^(22) m^(−3),thereby enhancing the system capability for future fueling experiments on EAST.This study offers valuable insights into CTI modification and the improvement of central fueling systems for prospective fusion reactors.展开更多
Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support v...Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.展开更多
Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscul...Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability.To counter this limitation,this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat.We employ FastAI vision-learner-based convolutional neural networks(CNNs)that include ResNet,DenseNet,VGG,ConvNeXt,SqueezeNet,and AlexNet to classify heart sound recordings.Instead of raw waveform analysis,the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms,which are suited for capturing temporal and frequency-wise patterns.The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations,noise levels,and acoustic distortions.To demonstrate generalization,external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds.Comparative analysis revealed that DenseNet-201,ConvNext Large,and ResNet-152 could deliver superior performance to the other architectures,achieving an accuracy of 81.50%,a precision of 85.50%,and an F1-score of 84.50%.In the process,we performed statistical significance testing,such as the Wilcoxon signed-rank test,to validate performance improvements over traditional classification methods.Beyond the technical contributions,the research underscores clinical integration,outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows.We also discuss in detail issues of computational efficiency,model interpretability,and ethical considerations,particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings.The study describes a scalable,automated system combining deep learning,feature extraction using spectrograms,and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease.AI-driven solutions can be viable in improving access,reducing delays in diagnosis,and ultimately even the continued global burden of heart disease.展开更多
The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated cha...The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated challenges,such as data security,interoperability,and ethical concerns,is crucial to realizing the full potential of IoT in healthcare.Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems.In this context,this paper presents a novelmethod for healthcare data privacy analysis.The technique is based on the identification of anomalies in cloud-based Internet of Things(IoT)networks,and it is optimized using explainable artificial intelligence.For anomaly detection,the Radial Boltzmann Gaussian Temporal Fuzzy Network(RBGTFN)is used in the process of doing information privacy analysis for healthcare data.Remora Colony SwarmOptimization is then used to carry out the optimization of the network.The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study.This evaluation suggested that themodel measures the accuracy,precision,latency,Quality of Service(QoS),and scalability of themodel.A remarkable 95%precision,93%latency,89%quality of service,98%detection accuracy,and 96%scalability were obtained by the suggested model,as shown by the subsequent findings.展开更多
The management of early stage hepatocellular carcinoma(HCC)presents significant challenges.While radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter ho...The management of early stage hepatocellular carcinoma(HCC)presents significant challenges.While radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter hospital stays,its high recurrence rate remains a significant impediment.Consequently,achieving improved survival solely through RFA is challenging,particularly in retrospective studies with inherent biases.Ultrasound is commonly used for guiding percutaneous RFA,but its low contrast can lead to missed tumors and the risk of HCC recurrence.To enhance the efficiency of ultrasound-guided percutaneous RFA,various techniques such as artificial ascites and contrast-enhanced ultrasound have been developed to facilitate complete tumor ablation.Minimally invasive surgery(MIS)offers advantages over open surgery and has gained traction in various surgical fields.Recent studies suggest that laparoscopic intraoperative RFA(IORFA)may be more effective than percutaneous RFA in terms of survival for HCC patients unsuitable for surgery,highlighting its significance.Therefore,combining MIS-IORFA with these enhanced percutaneous RFA techniques may hold greater significance for HCC treatment using the MIS-IORFA approach.This article reviews liver resection and RFA in HCC treatment,comparing their merits and proposing a trajectory involving their combination in future therapy.展开更多
This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration vs.exploitation,and a lac...This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration vs.exploitation,and a lack of thorough exploitation depth.To tackle these shortcomings,it proposes enhancements from three distinct perspectives:an initialization technique for populations grounded in opposition-based learning,a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration,and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators.The effectiveness of the Improved Harris Hawks Optimization algorithm(IHHO)is assessed by comparing it to five leading algorithms across 23 benchmark test functions.Experimental findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving capabilities.Additionally,this paper introduces a feature selection method leveraging the IHHO algorithm(IHHO-FS)to address challenges such as low efficiency in feature selection and high computational costs(time to find the optimal feature combination and model response time)associated with high-dimensional datasets.Comparative analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight datasets.The results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality,while also enhancing the efficiency of feature selection.Furthermore,IHHO-FS shows strong competitiveness relative to numerous algorithms.展开更多
A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are con...A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.展开更多
Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,which allows us to hide sensitive data in image files.In this paper,we propose a novel high-fidelity ...Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,which allows us to hide sensitive data in image files.In this paper,we propose a novel high-fidelity reversible data hiding scheme.Based on the advantage of the multipredictor mechanism,we combine two effective prediction schemes to improve prediction accuracy.In addition,the multihistogram technique is utilized to further improve the image quality of the stego image.Moreover,a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram.Experimental results show that the quality of the stego image of our scheme outperforms state-of-the-art schemes in most cases.展开更多
Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detec...Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.展开更多
Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial ...Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.展开更多
The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation fo...The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.展开更多
Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive data.This study introduces a phishing email detection framework that combines Bidirectional ...Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive data.This study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information systems.BERT’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing detection.Achieving an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing emails.This approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.展开更多
This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart ...This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.展开更多
Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num...Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.展开更多
基金This project was funded by Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah underGrant No.(IFPIP-1127-611-1443)the authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.
基金supported by the National Science and Technology Council under grant number 113-2221-E-027-126-MY3.
文摘In recent years,cyber threats have escalated across diverse sectors,with cybercrime syndicates increasingly exploiting system vulnerabilities.Traditional passive defense mechanisms have proven insufficient,particularly as Linux platforms—historically overlooked in favor of Windows—have emerged as frequent targets.According to Trend Micro,there has been a substantial increase in Linux-targeted malware,with ransomware attacks on Linux surpassing those on macOS.This alarming trend underscores the need for detection strategies specifically designed for Linux environments.To address this challenge,this study proposes a comprehensive malware detection framework tailored for Linux systems,integrating dynamic behavioral analysis with the semantic reasoning capabilities of large language models(LLMs).Malware samples are executed within sandbox environments to extract behavioral features such as system calls and command-line executions.These features are then systematically mapped to the MITRE ATT&CK framework,incorporating its defined data sources,data components,and Tactics,Techniques,and Procedures(TTPs).Two mapping constructs—Conceptual Definition Mapping and TTP Technical Keyword Mapping—are developed from official MITRE documentation.These resources are utilized to fine-tune an LLM,enabling it to semantically interpret complex behavioral patterns and infer associated attack techniques,including those employed by previously unknown malware variants.The resulting detection pipeline effectively bridges raw behavioral data with structured threat intelligence.Experimental evaluations confirm the efficacy of the proposed system,with the fine-tuned Gemma 2B model demonstrating significantly enhanced accuracy in associating behavioral features with ATT&CK-defined techniques.This study contributes a fully integrated Linux-specific detection framework,a novel approach for transforming unstructured behavioral data into actionable intelligence,improved interpretability of malicious behavior,and a scalable training process for future applications of LLMs in cybersecurity.
基金supported by the National Natural Science Foundation of China(Grant No.52331012)the Natural Science Foundation of Shanghai Municipality(Grant No.21ZR1426500)the Program for Cultivation of Graduate Students’Top-notch Innovative Talents of Shanghai Maritime University(Grant No.2023YBR007).
文摘As is known,centralized federated learning faces risks of a single point of failure and privacy breaches,and blockchain-based federated learning frameworks can address these challenges to a certain extent in recent works.However,malicious clients may still illegally access the blockchain to upload malicious data or steal on-chain data.In addition,blockchain-based federated training suffers from a heavy storage burden and excessive network communication overhead.To address these issues,we propose an asynchronous,tiered federated learning storage scheme based on blockchain and IPFS.It manages the execution of federated learning tasks through smart contracts deployed on the blockchain,decentralizing the entire training process.Additionally,the scheme employs a secure and efficient blockchain-based asynchronous tiered architecture,integrating attribute-based access control technology for resource exchange between the clients and the blockchain network.It dynamically manages access control policies during training and adopts a hybrid data storage strategy combining blockchain and IPFS.Experiments with multiple sets of image classification tasks are conducted,indicating that the storage strategy used in this scheme saves nearly 50 percent of the communication overhead and significantly reduces the on-chain storage burden compared to the traditional blockchain-only storage strategy.In terms of training effectiveness,it maintains similar accuracy as centralized training and minimizes the probability of being attacked.
基金supported by the Fujian University of Technology under Grant GYZ20016,GY-Z18183,and GY-Z19005partially supported by the National Science and Technology Council under Grant NSTC 113-2221-E-224-056-.
文摘To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.
基金supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant Nos.KJQN202100812,KJQN202215901,KJQN202400812).
文摘As smart manufacturing and Industry 4.0 continue to evolve,fault diagnosis of mechanical equipment has become crucial for ensuring production safety and optimizing equipment utilization.To address the challenge of cross-domain adaptation in intelligent diagnostic models under varying operational conditions,this paper introduces the CNN-1D-KAN model,which combines a 1D Convolutional Neural Network(1D-CNN)with a Kolmogorov–Arnold Network(KAN).The novelty of this approach lies in replacing the traditional 1D-CNN’s final fully connected layer with a KANLinear layer,leveraging KAN’s advanced nonlinear processing and function approximation capabilities while maintaining the simplicity of linear transformations.Experimental results on the CWRU dataset demonstrate that,under stable load conditions,the CNN-1D-KAN model achieves high accuracy,averaging 96.67%.Furthermore,the model exhibits strong transfer generalization and robustness across varying load conditions,sustaining an average accuracy of 90.21%.When compared to traditional neural networks(e.g.,1D-CNN and Multi-Layer Perceptron)and other domain adaptation models(e.g.,KAN Convolutions and KAN),the CNN-1D-KAN consistently outperforms in both accuracy and F1 scores across diverse load scenarios.Particularly in handling complex cross-domain data,it excels in diagnostic performance.This study provides an effective solution for cross-domain fault diagnosis in Industrial Internet systems,offering a theoretical foundation to enhance the reliability and stability of intelligent manufacturing processes,thus supporting the future advancement of industrial IoT applications.
基金supported by the National MCF Energy R&D Program of China(Nos.2024YFE03130001 and 2024YFE03130002)the Institute of Energy,Hefei Comprehensive National Science Center(Anhui Energy Laboratory)(Nos.21KZS202 and 23KHH140)+3 种基金the University Synergy Innovation Program of Anhui Province(Nos.GXXT-2021-014 and GXXT-2021-029)National Natural Science Foundation of China(Nos.12105088 and 12305247)the Fundamental Research Funds for the Central Universities of China(No.PA2024GDSK0097)the Anhui Province Key Research and Development Plan Program(Nos.202304a05020006 and 2021006).
文摘The compact torus injector(CTI)for the central fueling of the EAST tokamak has undergone significant upgrades to enhance its injection capability.During the initial phase of the platform testing phase,EAST-CTI demonstrated relatively low performance,with a maximum velocity of 150 km s^(−1) and a single compact torus(CT)plasma mass of 90μg[Kong D et al 2023 Plasma Sci.Technol.25065601].These parameters were insufficient for conducting central fueling experiments on the EAST tokamak.Consequently,extensive upgrades were carried out to improve the performance of the EAST-CTI system.The compression region was extended from 280 mm to 700 mm to prevent rapid compression and deceleration of the CT plasma,along with an extension of the acceleration region to further increase the plasma acceleration.The power supply system has also been upgraded.These improvements elevated the operating voltage from 8 kV to 15 kV,increased the discharge current from 120 kA to 300 kA and enabled repetitive operation at a maximum rate of 2 Hz.As a result,significant advances in EAST-CTI performance were achieved,with the maximum velocity increasing to 330 km s^(−1) and the CT plasma density reaching 1.5×10^(22) m^(−3),thereby enhancing the system capability for future fueling experiments on EAST.This study offers valuable insights into CTI modification and the improvement of central fueling systems for prospective fusion reactors.
基金funded by Institutional Fund Projects under grant no.(IFPDP-261-22)。
文摘Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.
基金funded by the deanship of scientific research(DSR),King Abdulaziz University,Jeddah,under grant No.(G-1436-611-309).
文摘Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability.To counter this limitation,this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat.We employ FastAI vision-learner-based convolutional neural networks(CNNs)that include ResNet,DenseNet,VGG,ConvNeXt,SqueezeNet,and AlexNet to classify heart sound recordings.Instead of raw waveform analysis,the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms,which are suited for capturing temporal and frequency-wise patterns.The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations,noise levels,and acoustic distortions.To demonstrate generalization,external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds.Comparative analysis revealed that DenseNet-201,ConvNext Large,and ResNet-152 could deliver superior performance to the other architectures,achieving an accuracy of 81.50%,a precision of 85.50%,and an F1-score of 84.50%.In the process,we performed statistical significance testing,such as the Wilcoxon signed-rank test,to validate performance improvements over traditional classification methods.Beyond the technical contributions,the research underscores clinical integration,outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows.We also discuss in detail issues of computational efficiency,model interpretability,and ethical considerations,particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings.The study describes a scalable,automated system combining deep learning,feature extraction using spectrograms,and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease.AI-driven solutions can be viable in improving access,reducing delays in diagnosis,and ultimately even the continued global burden of heart disease.
基金funded by Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah under grant No.(RG-6-611-43)the authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated challenges,such as data security,interoperability,and ethical concerns,is crucial to realizing the full potential of IoT in healthcare.Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems.In this context,this paper presents a novelmethod for healthcare data privacy analysis.The technique is based on the identification of anomalies in cloud-based Internet of Things(IoT)networks,and it is optimized using explainable artificial intelligence.For anomaly detection,the Radial Boltzmann Gaussian Temporal Fuzzy Network(RBGTFN)is used in the process of doing information privacy analysis for healthcare data.Remora Colony SwarmOptimization is then used to carry out the optimization of the network.The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study.This evaluation suggested that themodel measures the accuracy,precision,latency,Quality of Service(QoS),and scalability of themodel.A remarkable 95%precision,93%latency,89%quality of service,98%detection accuracy,and 96%scalability were obtained by the suggested model,as shown by the subsequent findings.
基金Supported by Feng Chia University/Chung Shan Medical University,No.FCU/CSMU 112-001(to Peng CM and Liu YJ)Taiwan National Science and Technology Council,No.111-2314-B-035-001-MY3Taichung Armed Forces General Hospital,No.107A42.
文摘The management of early stage hepatocellular carcinoma(HCC)presents significant challenges.While radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter hospital stays,its high recurrence rate remains a significant impediment.Consequently,achieving improved survival solely through RFA is challenging,particularly in retrospective studies with inherent biases.Ultrasound is commonly used for guiding percutaneous RFA,but its low contrast can lead to missed tumors and the risk of HCC recurrence.To enhance the efficiency of ultrasound-guided percutaneous RFA,various techniques such as artificial ascites and contrast-enhanced ultrasound have been developed to facilitate complete tumor ablation.Minimally invasive surgery(MIS)offers advantages over open surgery and has gained traction in various surgical fields.Recent studies suggest that laparoscopic intraoperative RFA(IORFA)may be more effective than percutaneous RFA in terms of survival for HCC patients unsuitable for surgery,highlighting its significance.Therefore,combining MIS-IORFA with these enhanced percutaneous RFA techniques may hold greater significance for HCC treatment using the MIS-IORFA approach.This article reviews liver resection and RFA in HCC treatment,comparing their merits and proposing a trajectory involving their combination in future therapy.
基金supported by the National Natural Science Foundation of China(grant number 62073330)constituted a segment of a project associated with the School of Computer Science and Information Engineering at Harbin Normal University。
文摘This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration vs.exploitation,and a lack of thorough exploitation depth.To tackle these shortcomings,it proposes enhancements from three distinct perspectives:an initialization technique for populations grounded in opposition-based learning,a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration,and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators.The effectiveness of the Improved Harris Hawks Optimization algorithm(IHHO)is assessed by comparing it to five leading algorithms across 23 benchmark test functions.Experimental findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving capabilities.Additionally,this paper introduces a feature selection method leveraging the IHHO algorithm(IHHO-FS)to address challenges such as low efficiency in feature selection and high computational costs(time to find the optimal feature combination and model response time)associated with high-dimensional datasets.Comparative analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight datasets.The results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality,while also enhancing the efficiency of feature selection.Furthermore,IHHO-FS shows strong competitiveness relative to numerous algorithms.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62105004 and 52174141)the College Student Innovation and Entrepreneurship Fund Project(Grant No.202210361053)+1 种基金Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center,Anhui University of Science&Technology(Grant No.KSJD202304)the Anhui Province Digital Agricultural Engineering Technology Research Center Open Project(Grant No.AHSZNYGC-ZXKF021)。
文摘A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.
基金funded by National Science Council,Taiwan,the Grant Number is NSC 111-2410-H-167-005-MY2.
文摘Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,which allows us to hide sensitive data in image files.In this paper,we propose a novel high-fidelity reversible data hiding scheme.Based on the advantage of the multipredictor mechanism,we combine two effective prediction schemes to improve prediction accuracy.In addition,the multihistogram technique is utilized to further improve the image quality of the stego image.Moreover,a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram.Experimental results show that the quality of the stego image of our scheme outperforms state-of-the-art schemes in most cases.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R 343)PrincessNourah bint Abdulrahman University,Riyadh,Saudi ArabiaDeanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia,for funding this researchwork through the project number“NBU-FFR-2024-1092-02”.
文摘Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.
基金supported by the MOE(Ministry of Education of China)Project of Humanities and Social Sciences(23YJAZH169)the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(T2020017)Henan Foreign Experts Project No.HNGD2023027.
文摘Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.
文摘The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.
基金supported by a grant from Hong Kong Metropolitan University (RD/2023/2.3)supported Princess Nourah bint Abdulrah-man University Researchers Supporting Project number (PNURSP2024R 343)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia for funding this research work through the project number“NBU-FFR-2024-1092-09”.
文摘Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive data.This study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information systems.BERT’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing detection.Achieving an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing emails.This approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.
基金support from the National Science and Technology Council of Taiwan(Contract Nos.111-2221 E-011081 and 111-2622-E-011019)the support from Intelligent Manufacturing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei,Taiwan,which is a Featured Areas Research Center in Higher Education Sprout Project of Ministry of Education(MOE),Taiwan(since 2023)was appreciatedWe also thank Wang Jhan Yang Charitable Trust Fund(Contract No.WJY 2020-HR-01)for its financial support.
文摘This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.
文摘Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
