One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrom...One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.展开更多
在高速通信过程中,数据处理系统通常需要数据缓存来实时存储收到的数据。利用现场可编程门阵列(field programmable gate array,FPGA)内部资源构建的先进先出(first in first out,FIFO),其容量有限,在数据通信过程中由于读写速度不匹配...在高速通信过程中,数据处理系统通常需要数据缓存来实时存储收到的数据。利用现场可编程门阵列(field programmable gate array,FPGA)内部资源构建的先进先出(first in first out,FIFO),其容量有限,在数据通信过程中由于读写速度不匹配而导致FIFO溢出,从而出现丢数现象。为此设计并实现了一种三级缓存结构,在FPGA外部引入1 MByte容量的静态随机存储器(static random access memory,SRAM)作为中间级缓存,输入级和输出级缓存为FPGA内部的FIFO,FPGA控制数据的传输和对SRAM的读写操作。采用三级缓存技术,简化了硬件复杂度,提高了设计的可实现性,经多次测试表明,本技术稳定可靠。展开更多
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through Project Number WE-44-0033.
文摘One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.
文摘在高速通信过程中,数据处理系统通常需要数据缓存来实时存储收到的数据。利用现场可编程门阵列(field programmable gate array,FPGA)内部资源构建的先进先出(first in first out,FIFO),其容量有限,在数据通信过程中由于读写速度不匹配而导致FIFO溢出,从而出现丢数现象。为此设计并实现了一种三级缓存结构,在FPGA外部引入1 MByte容量的静态随机存储器(static random access memory,SRAM)作为中间级缓存,输入级和输出级缓存为FPGA内部的FIFO,FPGA控制数据的传输和对SRAM的读写操作。采用三级缓存技术,简化了硬件复杂度,提高了设计的可实现性,经多次测试表明,本技术稳定可靠。
