Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented reality.The narrow beam usag...Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented reality.The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links.In this research work,the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated.The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output(MIMO)which is utilized in a hyperdense environment called heterogeneous networks(HetNet).The optimization problem which arises while maximizing the Mean Opinion Score(MOS)is analyzed along with the QoE(Quality of Experience)metric by considering the Base Station(BS)powers in addition to the needed Quality of Service(QoS).Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature.Hence a deep reinforcement learning framework is developed for tackling the same opti-mization problem.In this work,a Fuzzy-based Deep Convolutional Neural Net-work(FDCNN)is proposed in addition to a Deep Reinforcing Learning Framework(DRLF)for extracting the features of highly correlated data.The investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base stations.The proposed work outperforms in terms of MOS with multiple antennas.展开更多
This study focuses on the urgent requirement for improved accuracy in diseasemodeling by introducing a newcomputational framework called the Hybrid SIR-Fuzzy Model.By integrating the traditional Susceptible-Infectious...This study focuses on the urgent requirement for improved accuracy in diseasemodeling by introducing a newcomputational framework called the Hybrid SIR-Fuzzy Model.By integrating the traditional Susceptible-Infectious-Recovered(SIR)modelwith fuzzy logic,ourmethod effectively addresses the complex nature of epidemic dynamics by accurately accounting for uncertainties and imprecisions in both data and model parameters.The main aim of this research is to provide a model for disease transmission using fuzzy theory,which can successfully address uncertainty in mathematical modeling.Our main emphasis is on the imprecise transmission rate parameter,utilizing a three-part description of its membership level.This enhances the representation of disease processes with greater complexity and tackles the difficulties related to quantifying uncertainty in mathematical models.We investigate equilibrium points for three separate scenarios and perform a comprehensive sensitivity analysis,providing insight into the complex correlation betweenmodel parameters and epidemic results.In order to facilitate a quantitative analysis of the fuzzy model,we propose the implementation of a resilient numerical scheme.The convergence study of the scheme demonstrates its trustworthiness,providing a conditionally positive solution,which represents a significant improvement compared to current forward Euler schemes.The numerical findings demonstrate themodel’s effectiveness in accurately representing the dynamics of disease transmission.Significantly,when the mortality coefficient rises,both the susceptible and infected populations decrease,highlighting the model’s sensitivity to important epidemiological factors.Moreover,there is a direct relationship between higher Holling type rate values and a decrease in the number of individuals who are infected,as well as an increase in the number of susceptible individuals.This correlation offers a significant understanding of how many elements affect the consequences of an epidemic.Our objective is to enhance decision-making in public health by providing a thorough quantitative analysis of the Hybrid SIR-Fuzzy Model.Our approach not only tackles the existing constraints in disease modeling,but also paves the way for additional investigation,providing a vital instrument for researchers and policymakers alike.展开更多
Pancreatic cancer is one of the deadliest cancers,with less than 9%survival rates.Pancreatic Ductal Adeno Carcinoma(PDAC)is common with the general public affecting most people older than 45.Early detection of PDAC is...Pancreatic cancer is one of the deadliest cancers,with less than 9%survival rates.Pancreatic Ductal Adeno Carcinoma(PDAC)is common with the general public affecting most people older than 45.Early detection of PDAC is often challenging because cancer symptoms will progress only at later stages(advanced stage).One of the earlier symptoms of PDAC is Jaundice.Patients with diabetes,obesity,and alcohol consumption are also at higher risk of having pancreatic cancer.A decision support system is developed to detect pancreatic cancer at an earlier stage to address this challenge.Features such as Mean Hue,Mean Saturation,Mean Value,and Mean Standard Deviation are computed after color space conversion from RGB to HSV.Fuzzy k-Nearest Neighbor(F-kNN)is designed for classification.The system proposed is trained and tested using features extracted from jaundiced eye images.The proposed system results indicate that this model can predict pancreatic cancer as earlier as possible,helping clinicians make better decisions for surgical planning.展开更多
Background Understanding the volume fraction of water-oil-gas three-phase flow is of significant importance in oil and gas industry.Purpose The current research attempts to indicate the ability of adaptive network-bas...Background Understanding the volume fraction of water-oil-gas three-phase flow is of significant importance in oil and gas industry.Purpose The current research attempts to indicate the ability of adaptive network-based fuzzy inference system(ANFIS)to forecast the volume fractions in a water-oil-gas three-phase flow system.Method The current investigation devotes to measure the volume fractions in the stratified three-phase flow,on the basis of a dual-energy metering system consisting of the 152Eu and 137Cs and one NaI detector using ANFIS.The summation of volume fractions is equal to 100%and is also a constant,and this is enough for the ANFIS just to forecast two volume fractions.In the paper,three ANFIS models are employed.The first network is applied to forecast the oil and water volume fractions.The next to forecast the water and gas volume fractions,and the last to forecast the gas and oil volume fractions.For the next step,ANFIS networks are trained based on numerical simulation data from MCNP-X code.Results The accuracy of the nets is evaluated through the calculation of average testing error.The average errors are then compared.The model in which predictions has the most consistency with the numerical simulation results is selected as the most accurate predictor model.Based on the results,the best ANFIS net forecasts the water and gas volume fractions with the mean error of less than 0.8%.Conclusion The proposed methodology indicates that ANFIS can precisely forecast the volume fractions in a water-oil-gas three-phase flow system.展开更多
文摘Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented reality.The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links.In this research work,the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated.The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output(MIMO)which is utilized in a hyperdense environment called heterogeneous networks(HetNet).The optimization problem which arises while maximizing the Mean Opinion Score(MOS)is analyzed along with the QoE(Quality of Experience)metric by considering the Base Station(BS)powers in addition to the needed Quality of Service(QoS).Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature.Hence a deep reinforcement learning framework is developed for tackling the same opti-mization problem.In this work,a Fuzzy-based Deep Convolutional Neural Net-work(FDCNN)is proposed in addition to a Deep Reinforcing Learning Framework(DRLF)for extracting the features of highly correlated data.The investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base stations.The proposed work outperforms in terms of MOS with multiple antennas.
文摘This study focuses on the urgent requirement for improved accuracy in diseasemodeling by introducing a newcomputational framework called the Hybrid SIR-Fuzzy Model.By integrating the traditional Susceptible-Infectious-Recovered(SIR)modelwith fuzzy logic,ourmethod effectively addresses the complex nature of epidemic dynamics by accurately accounting for uncertainties and imprecisions in both data and model parameters.The main aim of this research is to provide a model for disease transmission using fuzzy theory,which can successfully address uncertainty in mathematical modeling.Our main emphasis is on the imprecise transmission rate parameter,utilizing a three-part description of its membership level.This enhances the representation of disease processes with greater complexity and tackles the difficulties related to quantifying uncertainty in mathematical models.We investigate equilibrium points for three separate scenarios and perform a comprehensive sensitivity analysis,providing insight into the complex correlation betweenmodel parameters and epidemic results.In order to facilitate a quantitative analysis of the fuzzy model,we propose the implementation of a resilient numerical scheme.The convergence study of the scheme demonstrates its trustworthiness,providing a conditionally positive solution,which represents a significant improvement compared to current forward Euler schemes.The numerical findings demonstrate themodel’s effectiveness in accurately representing the dynamics of disease transmission.Significantly,when the mortality coefficient rises,both the susceptible and infected populations decrease,highlighting the model’s sensitivity to important epidemiological factors.Moreover,there is a direct relationship between higher Holling type rate values and a decrease in the number of individuals who are infected,as well as an increase in the number of susceptible individuals.This correlation offers a significant understanding of how many elements affect the consequences of an epidemic.Our objective is to enhance decision-making in public health by providing a thorough quantitative analysis of the Hybrid SIR-Fuzzy Model.Our approach not only tackles the existing constraints in disease modeling,but also paves the way for additional investigation,providing a vital instrument for researchers and policymakers alike.
文摘Pancreatic cancer is one of the deadliest cancers,with less than 9%survival rates.Pancreatic Ductal Adeno Carcinoma(PDAC)is common with the general public affecting most people older than 45.Early detection of PDAC is often challenging because cancer symptoms will progress only at later stages(advanced stage).One of the earlier symptoms of PDAC is Jaundice.Patients with diabetes,obesity,and alcohol consumption are also at higher risk of having pancreatic cancer.A decision support system is developed to detect pancreatic cancer at an earlier stage to address this challenge.Features such as Mean Hue,Mean Saturation,Mean Value,and Mean Standard Deviation are computed after color space conversion from RGB to HSV.Fuzzy k-Nearest Neighbor(F-kNN)is designed for classification.The system proposed is trained and tested using features extracted from jaundiced eye images.The proposed system results indicate that this model can predict pancreatic cancer as earlier as possible,helping clinicians make better decisions for surgical planning.
文摘Background Understanding the volume fraction of water-oil-gas three-phase flow is of significant importance in oil and gas industry.Purpose The current research attempts to indicate the ability of adaptive network-based fuzzy inference system(ANFIS)to forecast the volume fractions in a water-oil-gas three-phase flow system.Method The current investigation devotes to measure the volume fractions in the stratified three-phase flow,on the basis of a dual-energy metering system consisting of the 152Eu and 137Cs and one NaI detector using ANFIS.The summation of volume fractions is equal to 100%and is also a constant,and this is enough for the ANFIS just to forecast two volume fractions.In the paper,three ANFIS models are employed.The first network is applied to forecast the oil and water volume fractions.The next to forecast the water and gas volume fractions,and the last to forecast the gas and oil volume fractions.For the next step,ANFIS networks are trained based on numerical simulation data from MCNP-X code.Results The accuracy of the nets is evaluated through the calculation of average testing error.The average errors are then compared.The model in which predictions has the most consistency with the numerical simulation results is selected as the most accurate predictor model.Based on the results,the best ANFIS net forecasts the water and gas volume fractions with the mean error of less than 0.8%.Conclusion The proposed methodology indicates that ANFIS can precisely forecast the volume fractions in a water-oil-gas three-phase flow system.
