Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by...Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be diabetic.There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’lives.Due to its rapid development,deep learning(DL)was used to predict numerous diseases.However,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization.Therefore,the selection of hyper-parameters is critical in improving classification performance.This study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters optimization.Two issues have been investigated and solved during the experiment to enhance the results.The first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)technique.The second issue is the model’s poor performance,which has been solved using the Bayesian optimization algorithm.The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.展开更多
The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective ...The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.展开更多
With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves stor...With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves storage issues,it is challenging to realize secure sharing of records over the network.Medi-block record in the healthcare system has brought a new digitalization method for patients’medical records.This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital.It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record.This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records.It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing,such as using Blockchain technology,Access Control,Privacy-Preserving,Proxy Re-Encryption,and Service-On-Chain approach.Finally,we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.展开更多
Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites,accommodation,and food according to their interests.This objective makes it harder for tourists to decide ...Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites,accommodation,and food according to their interests.This objective makes it harder for tourists to decide and plan where to go and what to do.Aside from hiring a local guide,an option which is beyond most travelers’budgets,the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user reviews.Therefore,this work utilizes the prevalent recommender systems and mobile app technologies to overcome this issue.Accordingly,this study proposes location-aware personalized traveler assistance(LAPTA),a system which integrates user preferences and the global positioning system(GPS)to generate personalized and location-aware recommendations.That integration will enable the enhanced recommendation of the developed scheme relative to those from the traditional recommender systems used in customer ratings.Specifically,LAPTA separates the data obtained from Google locations into name and category tags.After the data separation,the system fetches the keywords from the user’s input according to the user’s past research behavior.The proposed system uses the K-Nearest algorithm to match the name and category tags with the user’s input to generate personalized suggestions.The system also provides suggestions on the basis of nearby popular attractions using the Google point of interest feature to enhance system usability.The experimental results showed that LAPTA could provide more reliable and accurate recommendations compared to the reviewed recommendation applications.展开更多
Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems.Many issues in this field still unsolved,so several modern anomaly detection methods struggle...Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems.Many issues in this field still unsolved,so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data.Such a phenomenon is referred to as the“curse of dimensionality”that affects traditional techniques in terms of both accuracy and performance.Thus,this research proposed a hybrid model based on Deep Autoencoder Neural Network(DANN)with five layers to reduce the difference between the input and output.The proposed model was applied to a real-world gas turbine(GT)dataset that contains 87620 columns and 56 rows.During the experiment,two issues have been investigated and solved to enhance the results.The first is the dataset class imbalance,which solved using SMOTE technique.The second issue is the poor performance,which can be solved using one of the optimization algorithms.Several optimization algorithms have been investigated and tested,including stochastic gradient descent(SGD),RMSprop,Adam and Adamax.However,Adamax optimization algorithm showed the best results when employed to train theDANNmodel.The experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%,F1-score of 0.9649,Area Under the Curve(AUC)rate of 0.9649,and a minimal loss function during the hybrid model training.展开更多
Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GAN...Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GANs,where GANs are used to classify generated images into real and fake and multiple classes,similar to a general multi-class classifier.However,GANs have a sophisticated design that can be challenging to train.This is because obtaining the proper set of parameters for all models-generator,discriminator,and classifier is complex.As a result,training a single GAN model for different datasets may not produce satisfactory results.Therefore,this study proposes an SGAN model(Semi-Supervised GAN Classifier).First,a baseline model was constructed.The model was then enhanced by leveraging the Sine-Cosine Algorithm and Synthetic Minority Oversampling Technique(SMOTE).SMOTE was used to address class imbalances in the dataset,while Sine Cosine Algorithm(SCA)was used to optimize the weights of the classifier models.The optimal set of hyperparameters(learning rate and batch size)were obtained using grid manual search.Four well-known benchmark datasets and a set of evaluation measures were used to validate the proposed model.The proposed method was then compared against existing models,and the results on each dataset were recorded and demonstrated the effectiveness of the proposed model.The proposed model successfully showed improved test accuracy scores of 1%,2%,15%,and 5%on benchmarking multimedia datasets;Modified National Institute of Standards and Technology(MNIST)digits,Fashion MNIST,Pneumonia Chest X-ray,and Facial Emotion Detection Dataset,respectively.展开更多
基金This research/paper was fully supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS(YUTP)Fundamental Research Grant Scheme(015LC0-311).
文摘Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be diabetic.There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’lives.Due to its rapid development,deep learning(DL)was used to predict numerous diseases.However,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization.Therefore,the selection of hyper-parameters is critical in improving classification performance.This study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters optimization.Two issues have been investigated and solved during the experiment to enhance the results.The first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)technique.The second issue is the model’s poor performance,which has been solved using the Bayesian optimization algorithm.The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.
基金supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS (YUTP)Fundamental Research Grant Scheme (YUTPFRG/015LC0-274)support by Researchers Supporting Project Number (RSP-2023/309),King Saud University,Riyadh,Saudi Arabia.
文摘The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.
文摘With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves storage issues,it is challenging to realize secure sharing of records over the network.Medi-block record in the healthcare system has brought a new digitalization method for patients’medical records.This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital.It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record.This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records.It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing,such as using Blockchain technology,Access Control,Privacy-Preserving,Proxy Re-Encryption,and Service-On-Chain approach.Finally,we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.
基金The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges(APC)of this publication.
文摘Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites,accommodation,and food according to their interests.This objective makes it harder for tourists to decide and plan where to go and what to do.Aside from hiring a local guide,an option which is beyond most travelers’budgets,the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user reviews.Therefore,this work utilizes the prevalent recommender systems and mobile app technologies to overcome this issue.Accordingly,this study proposes location-aware personalized traveler assistance(LAPTA),a system which integrates user preferences and the global positioning system(GPS)to generate personalized and location-aware recommendations.That integration will enable the enhanced recommendation of the developed scheme relative to those from the traditional recommender systems used in customer ratings.Specifically,LAPTA separates the data obtained from Google locations into name and category tags.After the data separation,the system fetches the keywords from the user’s input according to the user’s past research behavior.The proposed system uses the K-Nearest algorithm to match the name and category tags with the user’s input to generate personalized suggestions.The system also provides suggestions on the basis of nearby popular attractions using the Google point of interest feature to enhance system usability.The experimental results showed that LAPTA could provide more reliable and accurate recommendations compared to the reviewed recommendation applications.
基金This research/paper was fully supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS(YUTP)Fundamental Research Grant Scheme(YUTP-015LC0-123).
文摘Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems.Many issues in this field still unsolved,so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data.Such a phenomenon is referred to as the“curse of dimensionality”that affects traditional techniques in terms of both accuracy and performance.Thus,this research proposed a hybrid model based on Deep Autoencoder Neural Network(DANN)with five layers to reduce the difference between the input and output.The proposed model was applied to a real-world gas turbine(GT)dataset that contains 87620 columns and 56 rows.During the experiment,two issues have been investigated and solved to enhance the results.The first is the dataset class imbalance,which solved using SMOTE technique.The second issue is the poor performance,which can be solved using one of the optimization algorithms.Several optimization algorithms have been investigated and tested,including stochastic gradient descent(SGD),RMSprop,Adam and Adamax.However,Adamax optimization algorithm showed the best results when employed to train theDANNmodel.The experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%,F1-score of 0.9649,Area Under the Curve(AUC)rate of 0.9649,and a minimal loss function during the hybrid model training.
基金This research was supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS(YUTP)Fundamental Research Grant Scheme(YUTPFRG/015LC0-308).
文摘Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GANs,where GANs are used to classify generated images into real and fake and multiple classes,similar to a general multi-class classifier.However,GANs have a sophisticated design that can be challenging to train.This is because obtaining the proper set of parameters for all models-generator,discriminator,and classifier is complex.As a result,training a single GAN model for different datasets may not produce satisfactory results.Therefore,this study proposes an SGAN model(Semi-Supervised GAN Classifier).First,a baseline model was constructed.The model was then enhanced by leveraging the Sine-Cosine Algorithm and Synthetic Minority Oversampling Technique(SMOTE).SMOTE was used to address class imbalances in the dataset,while Sine Cosine Algorithm(SCA)was used to optimize the weights of the classifier models.The optimal set of hyperparameters(learning rate and batch size)were obtained using grid manual search.Four well-known benchmark datasets and a set of evaluation measures were used to validate the proposed model.The proposed method was then compared against existing models,and the results on each dataset were recorded and demonstrated the effectiveness of the proposed model.The proposed model successfully showed improved test accuracy scores of 1%,2%,15%,and 5%on benchmarking multimedia datasets;Modified National Institute of Standards and Technology(MNIST)digits,Fashion MNIST,Pneumonia Chest X-ray,and Facial Emotion Detection Dataset,respectively.
