While smart wearables and remote devices have improved the speed of diagnosis and treatment,they have also created significant cybersecurity risks,especially with regard to the confidentiality and integrity of medical...While smart wearables and remote devices have improved the speed of diagnosis and treatment,they have also created significant cybersecurity risks,especially with regard to the confidentiality and integrity of medical data.Because the primary means of operation for these Internet of Things(IoT)devices is constant data transmission,they are vulnerable to cyberthreats including Distributed Denial-of-Service(DDoS)assaults and data injection.This study suggests an AI-based Healthcare Cybersecurity System(AI-HCsS)that integrates blockchain tech-nology to mitigate these vulnerabilities and provide strong,real-time patient data and healthcare system pro-tection.A new architecture is shown to identify and counteract DDoS attacks on the cloud infrastructure,and blockchain is used for safe and unchangeable data storage.The system extracts statistical,raw,and enhanced entropy-based features after performing improved min-max normalization for data pre-processing.Then,for precise DDoS attack detection,a modified Parallel Convolutional Neural Network(PCNN)is used.The model's output is interpreted using the SHapley Additive exPlanations(SHAP)approach,which identifies important characteristics that affect detection performance in order to improve transparency and aid clinical decision-making.According to experimental results,the modified PCNN outperforms traditional methods with a high detection accuracy of 91.1%.In addition to bolstering the cybersecurity of healthcare IoT ecosystems,this in-tegrated solution guarantees the real-time defense of clinical systems and patient data against changing cyberthreats.展开更多
Machine learning(ML)integrated with density functional theory(DFT)calculations have recently been used to accelerate the design and discovery of single-atom catalysts(SACs)by establishing deep structure–activity rela...Machine learning(ML)integrated with density functional theory(DFT)calculations have recently been used to accelerate the design and discovery of single-atom catalysts(SACs)by establishing deep structure–activity relationships.The traditional ML models are always difficult to identify the structural differences among the single-atom systems with different modification methods,leading to the limitation of the potential application range.Aiming to the structural properties of several typical two-dimensional MA_(2)Z_(4)-based single-atom systems(bare MA_(2)Z_(4) and metal single-atom doped/supported MA_(2)Z_(4)),an improved crystal graph convolutional neural network(CGCNN)classification model was employed,instead of the traditional machine learning regression model,to address the challenge of incompatibility in the studied systems.The CGCNN model was optimized using crystal graph representation in which the geometric configuration was divided into active layer,surface layer,and bulk layer(ASB-GCNN).Through ML and DFT calculations,five potential single-atom hydrogen evolution reaction(HER)catalysts were screened from chemical space of 600 MA_(2)Z_(4)-based materials,especially V_(1)/HfSn_(2)N_(4)(S)with high stability and activity(Δ_(GH*)is 0.06 eV).Further projected density of states(pDOS)analysis in combination with the wave function analysis of the SAC-H bond revealed that the SAC-dz^(2)orbital coincided with the H-s orbital around the energy level of−2.50 eV,and orbital analysis confirmed the formation ofσbonds.This study provides an efficient multistep screening design framework of metal single-atom catalyst for HER systems with similar two-dimensional supports but different geometric configurations.展开更多
Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy ...Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy problems.To address this research objective,this paper proposes a prediction model based on kernel principal component analysis(KPCA),modified cuckoo search algorithm(MCS)and deep convolutional neural networks(DCNN).Firstly,KPCA is utilized to reduce the dimension of the feature,which aims to reduce the redundant input vectors.Then using MCS to optimize the parameters of DCNN.Finally,the photovoltaic power forecasting method of KPCA-MCS-DCNN is established.In order to verify the prediction performance of the proposed model,this paper selects a photovoltaic power station in China for example analysis.The results show that the new hybrid KPCA-MCS-DCNN model has higher prediction accuracy and better robustness.展开更多
Thyroid disease is a medical condition caused due to the excess release of thyroid hormone.It is released by the thyroid gland which is in front of the neck just below the larynx.Medical pictures such as X-rays and CT...Thyroid disease is a medical condition caused due to the excess release of thyroid hormone.It is released by the thyroid gland which is in front of the neck just below the larynx.Medical pictures such as X-rays and CT scans can,however,be used to diagnose it.In this proposed model,Deep Learning technology is used to detect thyroid diseases.A Convolution Neural Network(CNN)based modified ResNet architecture is employed to detectfive different types of thyroid diseases namely 1.Hypothyroid 2.Hyperthyroid 3.Thyroid cancer 4.Thyroiditis 5.Thyroid nodules.In the proposed work,the training method is enhanced using dual optimizers for better accuracy and results.Keras,a Python library that is high level runs as the main part of the Tensor Flow framework.It is used in the proposed work to implement deep learning techniques.The comparative analysis of the proposed model and the existing work helps to show that there is a great improvement in the performance metrics in classifying the type of thyroid disease.By applying Adam and SGD(Stochastic Gradient Descent)optimizers in the training phase of the proposed model it was identified that these increase the operational efficiency of the modified ResNet model.After retraining the model with SGD,the modified ResNet provides more accuracy of about 97%whereas the basic ResNet architecture attains 94%accuracy.A web-based frame-work is also developed which yields the type of thyroid disease as the output for a given input scanned image of the system.展开更多
文摘While smart wearables and remote devices have improved the speed of diagnosis and treatment,they have also created significant cybersecurity risks,especially with regard to the confidentiality and integrity of medical data.Because the primary means of operation for these Internet of Things(IoT)devices is constant data transmission,they are vulnerable to cyberthreats including Distributed Denial-of-Service(DDoS)assaults and data injection.This study suggests an AI-based Healthcare Cybersecurity System(AI-HCsS)that integrates blockchain tech-nology to mitigate these vulnerabilities and provide strong,real-time patient data and healthcare system pro-tection.A new architecture is shown to identify and counteract DDoS attacks on the cloud infrastructure,and blockchain is used for safe and unchangeable data storage.The system extracts statistical,raw,and enhanced entropy-based features after performing improved min-max normalization for data pre-processing.Then,for precise DDoS attack detection,a modified Parallel Convolutional Neural Network(PCNN)is used.The model's output is interpreted using the SHapley Additive exPlanations(SHAP)approach,which identifies important characteristics that affect detection performance in order to improve transparency and aid clinical decision-making.According to experimental results,the modified PCNN outperforms traditional methods with a high detection accuracy of 91.1%.In addition to bolstering the cybersecurity of healthcare IoT ecosystems,this in-tegrated solution guarantees the real-time defense of clinical systems and patient data against changing cyberthreats.
基金supported by the National Key R&D Program of China(2021YFA1500900)National Natural Science Foundation of China(U21A20298,22141001).
文摘Machine learning(ML)integrated with density functional theory(DFT)calculations have recently been used to accelerate the design and discovery of single-atom catalysts(SACs)by establishing deep structure–activity relationships.The traditional ML models are always difficult to identify the structural differences among the single-atom systems with different modification methods,leading to the limitation of the potential application range.Aiming to the structural properties of several typical two-dimensional MA_(2)Z_(4)-based single-atom systems(bare MA_(2)Z_(4) and metal single-atom doped/supported MA_(2)Z_(4)),an improved crystal graph convolutional neural network(CGCNN)classification model was employed,instead of the traditional machine learning regression model,to address the challenge of incompatibility in the studied systems.The CGCNN model was optimized using crystal graph representation in which the geometric configuration was divided into active layer,surface layer,and bulk layer(ASB-GCNN).Through ML and DFT calculations,five potential single-atom hydrogen evolution reaction(HER)catalysts were screened from chemical space of 600 MA_(2)Z_(4)-based materials,especially V_(1)/HfSn_(2)N_(4)(S)with high stability and activity(Δ_(GH*)is 0.06 eV).Further projected density of states(pDOS)analysis in combination with the wave function analysis of the SAC-H bond revealed that the SAC-dz^(2)orbital coincided with the H-s orbital around the energy level of−2.50 eV,and orbital analysis confirmed the formation ofσbonds.This study provides an efficient multistep screening design framework of metal single-atom catalyst for HER systems with similar two-dimensional supports but different geometric configurations.
文摘Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy problems.To address this research objective,this paper proposes a prediction model based on kernel principal component analysis(KPCA),modified cuckoo search algorithm(MCS)and deep convolutional neural networks(DCNN).Firstly,KPCA is utilized to reduce the dimension of the feature,which aims to reduce the redundant input vectors.Then using MCS to optimize the parameters of DCNN.Finally,the photovoltaic power forecasting method of KPCA-MCS-DCNN is established.In order to verify the prediction performance of the proposed model,this paper selects a photovoltaic power station in China for example analysis.The results show that the new hybrid KPCA-MCS-DCNN model has higher prediction accuracy and better robustness.
基金Dr. Deepak Dahiya would like to thank Deanship of Scientific Re-search at MajmaahUniversity for supporting his work under Project No. (R-2022-45)。
文摘Thyroid disease is a medical condition caused due to the excess release of thyroid hormone.It is released by the thyroid gland which is in front of the neck just below the larynx.Medical pictures such as X-rays and CT scans can,however,be used to diagnose it.In this proposed model,Deep Learning technology is used to detect thyroid diseases.A Convolution Neural Network(CNN)based modified ResNet architecture is employed to detectfive different types of thyroid diseases namely 1.Hypothyroid 2.Hyperthyroid 3.Thyroid cancer 4.Thyroiditis 5.Thyroid nodules.In the proposed work,the training method is enhanced using dual optimizers for better accuracy and results.Keras,a Python library that is high level runs as the main part of the Tensor Flow framework.It is used in the proposed work to implement deep learning techniques.The comparative analysis of the proposed model and the existing work helps to show that there is a great improvement in the performance metrics in classifying the type of thyroid disease.By applying Adam and SGD(Stochastic Gradient Descent)optimizers in the training phase of the proposed model it was identified that these increase the operational efficiency of the modified ResNet model.After retraining the model with SGD,the modified ResNet provides more accuracy of about 97%whereas the basic ResNet architecture attains 94%accuracy.A web-based frame-work is also developed which yields the type of thyroid disease as the output for a given input scanned image of the system.
