MANTA vascular closure device is an alternative vascular access closure device that is predominantly designed for large bore arteriotomy procedures.Its implementation to reduce morbidity and mortality following percut...MANTA vascular closure device is an alternative vascular access closure device that is predominantly designed for large bore arteriotomy procedures.Its implementation to reduce morbidity and mortality following percutaneous procedures including peripheral veno-arterial(VA)-extracorporeal membrane oxygenation(ECMO)in critically ill patients with various severe clinical conditions such as refractory cardiogenic shock remains to be under scientific discussion.The use of the MANTA vascular closure device leads to a sufficient reduction in a number of post-decannulation complications such as bleeding,vascular complications,inflammatory reactions and major amputation.Furthermore,the technical success of percutaneous decannulation of VA-ECMO with the MANTA vascular closure device appears to be safe and effective.It has been reported that MANTA vascular closure device exerted a strict similarity with other vascular surgical systems in safe profile regardless of the indication for its utilization.Overall,the immobilized patients achieved a favorable recovery outcome with MANTA including safe decannulation and low risk of vascular complications.The authors suggest the use of pulse wave distal Doppler technology for early detection of these clinically relevant complications.In conclusion,MANTA vascular closure device seems to be safe and effective technical approach to provide low-risk vascular assess for a long time for severe sick individuals.展开更多
Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resou...Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures.Nonetheless,sustaining elevated Quality of Service(QoS)in dynamic,resource-limited systems poses significant hurdles.This study introduces an innovative packet-based proactive end-to-end(ETE)resource management system that facilitates network slicing with improved resilience and proactivity.To get around the drawbacks of conventional reactive systems,we develop a cost-efficient slice provisioning architecture that takes into account limits on radio,processing,and transmission resources.The optimization issue is non-convex,NP-hard,and requires online resolution in a dynamic setting.We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning(DRL)methodology with an Improved Manta-Ray Foraging Optimization(ImpMRFO)algorithm.The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence,hence facilitating improved exploration-exploitation trade-offs.The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions,whereas the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions,while the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques.Our technique increases PSSRr,decreases average latency,and optimizes resource use.These results demonstrate that the hybrid architecture for robust,real-time,and scalable slice management in future 6G networks is feasible.展开更多
In this research paper,an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator(DFIG)based wind energy system has been proposed.The proposed strategy u...In this research paper,an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator(DFIG)based wind energy system has been proposed.The proposed strategy used the robust Fractional-Order(FO)Proportional-Integral(PI)control technique.The FOPI control contains a non-integer order which is preferred over the integer-order control owing to its benefits.It offers extra flexibility in design and demonstrates superior outcomes such as high robustness and effectiveness.The optimal gains of the FOPI controller have been determined using a recent Manta Ray Foraging Optimization(MRFO)algorithm.During the optimization process,the FOPI controller’s parameters are assigned to be the decision variables whereas the objective function is the error racking that to be minimized.To prove the superiority of the MRFO algorithm,an empirical comparison study with the homologous particle swarm optimization and genetic algorithm is achieved.The obtained results proved the superiority of the introduced strategy in tracking and control performances against various conditions such as voltage dips and wind speed variation.展开更多
This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic fields.Introduced in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing ins...This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic fields.Introduced in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault foraging.These biologically inspired strategies allow for effective solutions to intricate physical challenges.With its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization problems.Its utility and benefits have found traction in numerous academic sectors.Since its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference proceedings.This paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization challenges.Research trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.展开更多
Bionic manta underwater vehicles will play an essential role in future oceans and can perform tasks,such as long-duration reconnaissance and exploration,due to their efficient propulsion.The manta wings’deformation i...Bionic manta underwater vehicles will play an essential role in future oceans and can perform tasks,such as long-duration reconnaissance and exploration,due to their efficient propulsion.The manta wings’deformation is evident during the swimming process.To improve the propulsion performance of the unmanned submersible,the study of the deformation into the bionic pectoral fin is necessary.In this research,we designed and fabricated a flexible bionic pectoral fin,which is based on the Fin Ray®effect with active and passive deformation(APD)capability.The APD fin was actively controlled by two servo motors and could be passively deformed to variable degrees.The APD fin was moved at 0.5 Hz beat frequency,and the propulsive performance was experimentally verified of the bionic pectoral fins equipped with different extents of deformation.These results showed that the pectoral fin with active–passive deformed capabilities could achieve similar natural biological deformation in the wingspan direction.The average thrust(T)under the optimal wingspan deformation is 61.5%higher than the traditional passive deformed pectoral fins.The obtained results shed light on the design and optimization of the bionic pectoral fins to improve the propulsive performance of unmanned underwater vehicles(UUV).展开更多
BACKGROUND The MANTA vascular closure device(VCD)represents a novel approach to achieving hemostasis after large-bore femoral access procedures.Numerous clinical studies have evaluated the efficacy of the MANTA device...BACKGROUND The MANTA vascular closure device(VCD)represents a novel approach to achieving hemostasis after large-bore femoral access procedures.Numerous clinical studies have evaluated the efficacy of the MANTA device across a range of patient populations undergoing different procedures.However,there is still a paucity of data available concerning the use of MANTA devices in aiding the decannulation of venoarterial extracorporeal membrane oxygenation(VAECMO).AIM To present our single-center experience of utilizing the MANTA VCD in patients undergoing this procedure.METHODS This single-center study included all patients undergoing percutaneous decannulation of femoral VA-ECMO using the MANTA plug-based VCD between January 2021 and October 2023 at University Hospitals Cleveland Medical Center.Inclusion criteria were adult patients who required prolonged(>24 hours)hemodynamic support with VA-ECMO.Outcomes included all-cause mortality,hemostasis,bleeding,limb ischemia,and site infection.RESULTS This is a retrospective cohort study of 19 patients with a mean age of 56.8 years.Twelve of them were males with a mean body mass index of 29.The most common extracorporeal membrane oxygenation indication was acute coronary syndrome complicated by cardiogenic shock at 36.8%.The mean length of intensive care unit stay for these patients was 18.8±8.42 days.Seventeen out of 19 patients survived to discharge.The MANTA device was successfully deployed in 19 patients,with 10 procedures conducted at the bedside and 9 in an operating room setting.Complete hemostasis was achieved within 5 minutes of MANTA deployment in 17 out of 19 patients.In 2 patients manual compression after Manta deployment was required to achieve adequate hemostasis.Additionally,acute lower extremity ischemia was noted in two patients,necessitating endovascular interventions.No infections were reported at the site of MANTA deployment.CONCLUSION Overall,based on our experience and that of other centers,the MANTA VCD has proven to be a simple,safe,and effective percutaneous technique for facilitating in the OR,but most of all it opens the opportunity for bedside VAECMO decannulation.Post-decannulation ischemic complications are higher in this series of sick patients when compared with elective procedures like transcatheter aortic valve replacement and endovascular aneurysm repair.Additionally,operators should be mindful of the incidence of ischemic complications.Distal Doppler pulse signals should always be checked,to indicate bailout options when this occurs.展开更多
Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning thes...Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.展开更多
Fish in nature exhibit a variety of swimming modes such as forward swimming,backward swimming,turning,pitching,etc.,enabling them to swim in complex scenes such as coral reefs.It is still difficult for a robotic fish ...Fish in nature exhibit a variety of swimming modes such as forward swimming,backward swimming,turning,pitching,etc.,enabling them to swim in complex scenes such as coral reefs.It is still difficult for a robotic fish to swim autonomously in a confined area as a real fish.Here,we develop an untethered robotic manta as an experimental platform,which consists of two flexible pectoral fins and a tail fin,with three infrared sensors installed on the front,left,and right sides of the head to sense the surrounding obstacles.To generate multiple swimming modes of the robotic manta and online switching of different modes,we design a closed-loop Central Pattern Generator(CPG)controller based on distance information and use a combination of phase difference and amplitude of the CPG model to achieve stable and rapid adjustment of yaw angle.To verify the autonomous swimming ability of the robotic manta in complex scenes,we design an experimental scenario with a concave obstacle.The experimental results show that the robotic manta can achieve forward swimming,backward swimming,in situ turning within the concave obstacle,and finally exit from the area safely while relying on the perception of external obstacles,which can provide insight into the autonomous exploration of complex scenes by the biomimetic robotic fish.Finally,the swimming ability of the robotic manta is verified by field tests.展开更多
The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources.The developments of artificial intellig...The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources.The developments of artificial intelligence(AI)and machine learning(ML)models assist in the effectual design of medical data classification models.Therefore,this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-toSequence Autoencoder(OSAE-LSTM)model for biomedical data classification.The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases.Primarily,the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform format.Followed by,the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical data.At last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization process.The utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM model.The simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTM model over the other approaches under several dimensions.展开更多
A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious ta...A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.展开更多
文摘MANTA vascular closure device is an alternative vascular access closure device that is predominantly designed for large bore arteriotomy procedures.Its implementation to reduce morbidity and mortality following percutaneous procedures including peripheral veno-arterial(VA)-extracorporeal membrane oxygenation(ECMO)in critically ill patients with various severe clinical conditions such as refractory cardiogenic shock remains to be under scientific discussion.The use of the MANTA vascular closure device leads to a sufficient reduction in a number of post-decannulation complications such as bleeding,vascular complications,inflammatory reactions and major amputation.Furthermore,the technical success of percutaneous decannulation of VA-ECMO with the MANTA vascular closure device appears to be safe and effective.It has been reported that MANTA vascular closure device exerted a strict similarity with other vascular surgical systems in safe profile regardless of the indication for its utilization.Overall,the immobilized patients achieved a favorable recovery outcome with MANTA including safe decannulation and low risk of vascular complications.The authors suggest the use of pulse wave distal Doppler technology for early detection of these clinically relevant complications.In conclusion,MANTA vascular closure device seems to be safe and effective technical approach to provide low-risk vascular assess for a long time for severe sick individuals.
文摘Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures.Nonetheless,sustaining elevated Quality of Service(QoS)in dynamic,resource-limited systems poses significant hurdles.This study introduces an innovative packet-based proactive end-to-end(ETE)resource management system that facilitates network slicing with improved resilience and proactivity.To get around the drawbacks of conventional reactive systems,we develop a cost-efficient slice provisioning architecture that takes into account limits on radio,processing,and transmission resources.The optimization issue is non-convex,NP-hard,and requires online resolution in a dynamic setting.We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning(DRL)methodology with an Improved Manta-Ray Foraging Optimization(ImpMRFO)algorithm.The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence,hence facilitating improved exploration-exploitation trade-offs.The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions,whereas the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions,while the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques.Our technique increases PSSRr,decreases average latency,and optimizes resource use.These results demonstrate that the hybrid architecture for robust,real-time,and scalable slice management in future 6G networks is feasible.
文摘In this research paper,an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator(DFIG)based wind energy system has been proposed.The proposed strategy used the robust Fractional-Order(FO)Proportional-Integral(PI)control technique.The FOPI control contains a non-integer order which is preferred over the integer-order control owing to its benefits.It offers extra flexibility in design and demonstrates superior outcomes such as high robustness and effectiveness.The optimal gains of the FOPI controller have been determined using a recent Manta Ray Foraging Optimization(MRFO)algorithm.During the optimization process,the FOPI controller’s parameters are assigned to be the decision variables whereas the objective function is the error racking that to be minimized.To prove the superiority of the MRFO algorithm,an empirical comparison study with the homologous particle swarm optimization and genetic algorithm is achieved.The obtained results proved the superiority of the introduced strategy in tracking and control performances against various conditions such as voltage dips and wind speed variation.
文摘This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic fields.Introduced in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault foraging.These biologically inspired strategies allow for effective solutions to intricate physical challenges.With its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization problems.Its utility and benefits have found traction in numerous academic sectors.Since its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference proceedings.This paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization challenges.Research trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.
基金supported by the National Key Research and Development Program(Grant no.2022YFC2805200,2020YFB1313200)the National Natural Science Foundation of China(Grant no.52001260,52201381,52371338)Ningbo Natural Science Foundation(Grant no.2022J062).
文摘Bionic manta underwater vehicles will play an essential role in future oceans and can perform tasks,such as long-duration reconnaissance and exploration,due to their efficient propulsion.The manta wings’deformation is evident during the swimming process.To improve the propulsion performance of the unmanned submersible,the study of the deformation into the bionic pectoral fin is necessary.In this research,we designed and fabricated a flexible bionic pectoral fin,which is based on the Fin Ray®effect with active and passive deformation(APD)capability.The APD fin was actively controlled by two servo motors and could be passively deformed to variable degrees.The APD fin was moved at 0.5 Hz beat frequency,and the propulsive performance was experimentally verified of the bionic pectoral fins equipped with different extents of deformation.These results showed that the pectoral fin with active–passive deformed capabilities could achieve similar natural biological deformation in the wingspan direction.The average thrust(T)under the optimal wingspan deformation is 61.5%higher than the traditional passive deformed pectoral fins.The obtained results shed light on the design and optimization of the bionic pectoral fins to improve the propulsive performance of unmanned underwater vehicles(UUV).
文摘BACKGROUND The MANTA vascular closure device(VCD)represents a novel approach to achieving hemostasis after large-bore femoral access procedures.Numerous clinical studies have evaluated the efficacy of the MANTA device across a range of patient populations undergoing different procedures.However,there is still a paucity of data available concerning the use of MANTA devices in aiding the decannulation of venoarterial extracorporeal membrane oxygenation(VAECMO).AIM To present our single-center experience of utilizing the MANTA VCD in patients undergoing this procedure.METHODS This single-center study included all patients undergoing percutaneous decannulation of femoral VA-ECMO using the MANTA plug-based VCD between January 2021 and October 2023 at University Hospitals Cleveland Medical Center.Inclusion criteria were adult patients who required prolonged(>24 hours)hemodynamic support with VA-ECMO.Outcomes included all-cause mortality,hemostasis,bleeding,limb ischemia,and site infection.RESULTS This is a retrospective cohort study of 19 patients with a mean age of 56.8 years.Twelve of them were males with a mean body mass index of 29.The most common extracorporeal membrane oxygenation indication was acute coronary syndrome complicated by cardiogenic shock at 36.8%.The mean length of intensive care unit stay for these patients was 18.8±8.42 days.Seventeen out of 19 patients survived to discharge.The MANTA device was successfully deployed in 19 patients,with 10 procedures conducted at the bedside and 9 in an operating room setting.Complete hemostasis was achieved within 5 minutes of MANTA deployment in 17 out of 19 patients.In 2 patients manual compression after Manta deployment was required to achieve adequate hemostasis.Additionally,acute lower extremity ischemia was noted in two patients,necessitating endovascular interventions.No infections were reported at the site of MANTA deployment.CONCLUSION Overall,based on our experience and that of other centers,the MANTA VCD has proven to be a simple,safe,and effective percutaneous technique for facilitating in the OR,but most of all it opens the opportunity for bedside VAECMO decannulation.Post-decannulation ischemic complications are higher in this series of sick patients when compared with elective procedures like transcatheter aortic valve replacement and endovascular aneurysm repair.Additionally,operators should be mindful of the incidence of ischemic complications.Distal Doppler pulse signals should always be checked,to indicate bailout options when this occurs.
文摘Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.
基金supported by the National Key Research and Development Program(Grant No.2020YFB1313200,2022YFC2805200)the National Natural Science Foundation of China(Grant No.52001260,52201381)Ningbo Natural Science Foundation(Grant No.2022J062).
文摘Fish in nature exhibit a variety of swimming modes such as forward swimming,backward swimming,turning,pitching,etc.,enabling them to swim in complex scenes such as coral reefs.It is still difficult for a robotic fish to swim autonomously in a confined area as a real fish.Here,we develop an untethered robotic manta as an experimental platform,which consists of two flexible pectoral fins and a tail fin,with three infrared sensors installed on the front,left,and right sides of the head to sense the surrounding obstacles.To generate multiple swimming modes of the robotic manta and online switching of different modes,we design a closed-loop Central Pattern Generator(CPG)controller based on distance information and use a combination of phase difference and amplitude of the CPG model to achieve stable and rapid adjustment of yaw angle.To verify the autonomous swimming ability of the robotic manta in complex scenes,we design an experimental scenario with a concave obstacle.The experimental results show that the robotic manta can achieve forward swimming,backward swimming,in situ turning within the concave obstacle,and finally exit from the area safely while relying on the perception of external obstacles,which can provide insight into the autonomous exploration of complex scenes by the biomimetic robotic fish.Finally,the swimming ability of the robotic manta is verified by field tests.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R235)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR06).
文摘The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources.The developments of artificial intelligence(AI)and machine learning(ML)models assist in the effectual design of medical data classification models.Therefore,this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-toSequence Autoencoder(OSAE-LSTM)model for biomedical data classification.The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases.Primarily,the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform format.Followed by,the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical data.At last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization process.The utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM model.The simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTM model over the other approaches under several dimensions.
文摘A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.
