Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and avail...Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud services.However,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm.Furthermore,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud services.Moreover,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches.In this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and password.The underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers.The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.展开更多
Brain tumor identification is a challenging task in neuro-oncology.The brain’s complex anatomy makes it a crucial part of the central nervous system.Accurate tumor classification is crucial for clinical diagnosis and...Brain tumor identification is a challenging task in neuro-oncology.The brain’s complex anatomy makes it a crucial part of the central nervous system.Accurate tumor classification is crucial for clinical diagnosis and treatment planning.This research presents a significant advancement in the multi-classification of brain tumors.This paper proposed a novel architecture that integrates Enhanced ResNeXt 101_32×8d,a Convolutional Neural Network(CNN)with a multi-head self-attention(MHSA)mechanism.This combination harnesses the strengths of the feature extraction,feature representation by CNN,and long-range dependencies by MHSA.Magnetic Resonance Imaging(MRI)datasets were employed to check the effectiveness of the proposed architecture.The first dataset(DS-1,Msoud)included four brain tumor classes,and the second dataset(DS-2)contained seven brain tumor classes.This methodology effectively distinguished various tumor classes,achieving high accuracies of 99.75% on DS-1 and 98.80% on DS-2.These impressive results indicate the superior performance and adaptability of our model for multiclass brain tumor classification.Evaluationmetrics such as accuracy,precision,recall,F1 score,and ROC(receiver operating characteristic)curve were utilized to comprehensively evaluate model validity.The performance results showed that the model is well-suited for clinical applications,with reduced errors and high accuracy.展开更多
Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily life.With the increasing capabilities and accuracy of AI,the application of AI will have more impacts ...Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily life.With the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry 4.0.This study conducts a systematic literature review to study the state-of-the-art on AI in industry 4.0.This paper describes the development of industries and the evolution of AI.This paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry 4.0.The findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of AI.In the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.展开更多
Wrong-way driving(WWD)has been a long-lasting issue for transportation agencies and law enforcement,since it causes pivotal threats to road users.Notwithstanding being rare,crashes occurring due to WWD are more severe...Wrong-way driving(WWD)has been a long-lasting issue for transportation agencies and law enforcement,since it causes pivotal threats to road users.Notwithstanding being rare,crashes occurring due to WWD are more severe than other types of crashes.In order to analyze WWD crashes,there is a need to obtain WWD incidents or crash data.However,it is time-consuming to identify actual WWD crashes from potential WWD crashes in large crash databases.It often involves large man-hours to review hardcopy of crash narratives in the police reports.Otherwise,it may cause an overestimation or underestimation of WWD crash frequencies.To fill this gap,the present study,as the first-of-its-kind,aims at identifying actual WWD crashes from potential WWD crashes in police reports by using machine learning methods.Recently,Bidirectional Encoder Representations from Transformers(BERT)models have shown promising results in natural language processing.In this study,we implemented the BERT model as well as five conventional classification algorithms,including Naïve Bayes(NB),Support Vector Machine(SVM),Decision Tree(DT),Random Forest(RF),and Single Layer Perceptron(SLP)to classify crash report narratives as actual WWD and non-WWD crashes.Cross-validation and different performance metrics were used to evaluate the performance of each classification algorithm.Results indicated that the BERT model outperformed in identifying actual WWD crashes in comparison with other algorithms with an accuracy of 81.59%.The BERT classification algorithm can be implemented to reduce the time needed to identify actual WWD crashes from crash report narratives.展开更多
基金The research work was supported by UTP-Universitas Telkom,Indonesia International Collaborative Research Funding(ICRF)015ME0-153 and Center for Graduate Studies(CGS),Universiti Teknologi PETRONAS(UTP),Perak,Malaysia.
文摘Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud services.However,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm.Furthermore,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud services.Moreover,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches.In this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and password.The underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers.The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.
文摘Brain tumor identification is a challenging task in neuro-oncology.The brain’s complex anatomy makes it a crucial part of the central nervous system.Accurate tumor classification is crucial for clinical diagnosis and treatment planning.This research presents a significant advancement in the multi-classification of brain tumors.This paper proposed a novel architecture that integrates Enhanced ResNeXt 101_32×8d,a Convolutional Neural Network(CNN)with a multi-head self-attention(MHSA)mechanism.This combination harnesses the strengths of the feature extraction,feature representation by CNN,and long-range dependencies by MHSA.Magnetic Resonance Imaging(MRI)datasets were employed to check the effectiveness of the proposed architecture.The first dataset(DS-1,Msoud)included four brain tumor classes,and the second dataset(DS-2)contained seven brain tumor classes.This methodology effectively distinguished various tumor classes,achieving high accuracies of 99.75% on DS-1 and 98.80% on DS-2.These impressive results indicate the superior performance and adaptability of our model for multiclass brain tumor classification.Evaluationmetrics such as accuracy,precision,recall,F1 score,and ROC(receiver operating characteristic)curve were utilized to comprehensively evaluate model validity.The performance results showed that the model is well-suited for clinical applications,with reduced errors and high accuracy.
文摘Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily life.With the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry 4.0.This study conducts a systematic literature review to study the state-of-the-art on AI in industry 4.0.This paper describes the development of industries and the evolution of AI.This paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry 4.0.The findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of AI.In the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.
文摘Wrong-way driving(WWD)has been a long-lasting issue for transportation agencies and law enforcement,since it causes pivotal threats to road users.Notwithstanding being rare,crashes occurring due to WWD are more severe than other types of crashes.In order to analyze WWD crashes,there is a need to obtain WWD incidents or crash data.However,it is time-consuming to identify actual WWD crashes from potential WWD crashes in large crash databases.It often involves large man-hours to review hardcopy of crash narratives in the police reports.Otherwise,it may cause an overestimation or underestimation of WWD crash frequencies.To fill this gap,the present study,as the first-of-its-kind,aims at identifying actual WWD crashes from potential WWD crashes in police reports by using machine learning methods.Recently,Bidirectional Encoder Representations from Transformers(BERT)models have shown promising results in natural language processing.In this study,we implemented the BERT model as well as five conventional classification algorithms,including Naïve Bayes(NB),Support Vector Machine(SVM),Decision Tree(DT),Random Forest(RF),and Single Layer Perceptron(SLP)to classify crash report narratives as actual WWD and non-WWD crashes.Cross-validation and different performance metrics were used to evaluate the performance of each classification algorithm.Results indicated that the BERT model outperformed in identifying actual WWD crashes in comparison with other algorithms with an accuracy of 81.59%.The BERT classification algorithm can be implemented to reduce the time needed to identify actual WWD crashes from crash report narratives.
