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
The internet has become a part of every human life.Also,various devices that are connected through the internet are increasing.Nowadays,the Industrial Internet of things(IIoT)is an evolutionary technology interconnect...The internet has become a part of every human life.Also,various devices that are connected through the internet are increasing.Nowadays,the Industrial Internet of things(IIoT)is an evolutionary technology interconnecting various industries in digital platforms to facilitate their development.Moreover,IIoT is being used in various industrial fields such as logistics,manufacturing,metals and mining,gas and oil,transportation,aviation,and energy utilities.It is mandatory that various industrial fields require highly reliable security and preventive measures against cyber-attacks.Intrusion detection is defined as the detection in the network of security threats targeting privacy information and sensitive data.Intrusion Detection Systems(IDS)have taken an important role in providing security in the field of computer networks.Prevention of intrusion is completely based on the detection functions of the IDS.When an IIoT network expands,it generates a huge volume of data that needs an IDS to detect intrusions and prevent network attacks.Many research works have been done for preventing network attacks.Every day,the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly defined.In this regard,this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA(WDLDA).The implemented WDLDA results in a rate of detection accuracy(DRA)of 97%and a false positive rate(FPR)of 11%using the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)dataset.展开更多
基于IP(intellectual property)核的系统级芯片的测试已成为SoC(system on chip)发展中的瓶颈,提出了一种采用BBO(biogeography based optimization)算法的Wrapper扫描链设计方法,使得Wrapper扫描链均衡化,从而达到IP核测试时间最小化...基于IP(intellectual property)核的系统级芯片的测试已成为SoC(system on chip)发展中的瓶颈,提出了一种采用BBO(biogeography based optimization)算法的Wrapper扫描链设计方法,使得Wrapper扫描链均衡化,从而达到IP核测试时间最小化的目的。本算法基于群体智能,通过实施迁徙操作和变异操作,实现Wrapper扫描链均衡化设计。本文以ITC'02 Test bench-marks中的典型IP核为实验对象,实验结果表明本算法相比BFD(best fit decrease)等算法,能够进一步缩短Wrapper扫描链,从而缩短IP核测试时间。展开更多
测试问题已成为SoC发展过程中的瓶颈,提出一种新的Wrapper扫描链平衡算法以期缩短IP核测试时间。算法首先计算Wrapper扫描链长度平均值,再结合特定的余量值,计算得到一个取值区间,记该区间为平均值余量;然后将IP核的内部扫描链按其长度...测试问题已成为SoC发展过程中的瓶颈,提出一种新的Wrapper扫描链平衡算法以期缩短IP核测试时间。算法首先计算Wrapper扫描链长度平均值,再结合特定的余量值,计算得到一个取值区间,记该区间为平均值余量;然后将IP核的内部扫描链按其长度降序排列,每次均将最长的内部扫描链添加到某条Wrapper扫描链上,直到该Wrapper扫描链长度在平均值余量所指定的区间内为止。以ITC'02 SoC Test Benchmarks内的所有测试集为对象完成的实验证明本算法能极其有效的通过扫描链平衡设计缩短IP核测试时间。展开更多
提出用于均衡Wrapper扫描链的交换优化算法以及用于测试调度的局部最优算法,这两种算法依据测试总线空闲率(IBPTB)指标,可从IP层和系统顶层对系统芯片(SOC)测试时间实现联合优化,进而使SOC的测试时间大大降低.为了验证两种算法及其联合...提出用于均衡Wrapper扫描链的交换优化算法以及用于测试调度的局部最优算法,这两种算法依据测试总线空闲率(IBPTB)指标,可从IP层和系统顶层对系统芯片(SOC)测试时间实现联合优化,进而使SOC的测试时间大大降低.为了验证两种算法及其联合优化性能的有效性和可靠性,对基于ITC’02国际SOC基准电路进行了相关的验证试验.针对p93791基准电路中core6 IP核,交换优化算法能得到比经典BFD(best fit decreasing)算法更均衡的Wrapper扫描链,在最佳情况下最长Wrapper扫描链长度减少2.6%;针对d695基准电路,局部最优算法根据IP核的IBPTB指标,可使相应SOC的测试时间在最优时比经典整数线性规划(ILP)算法减少12.7%.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The internet has become a part of every human life.Also,various devices that are connected through the internet are increasing.Nowadays,the Industrial Internet of things(IIoT)is an evolutionary technology interconnecting various industries in digital platforms to facilitate their development.Moreover,IIoT is being used in various industrial fields such as logistics,manufacturing,metals and mining,gas and oil,transportation,aviation,and energy utilities.It is mandatory that various industrial fields require highly reliable security and preventive measures against cyber-attacks.Intrusion detection is defined as the detection in the network of security threats targeting privacy information and sensitive data.Intrusion Detection Systems(IDS)have taken an important role in providing security in the field of computer networks.Prevention of intrusion is completely based on the detection functions of the IDS.When an IIoT network expands,it generates a huge volume of data that needs an IDS to detect intrusions and prevent network attacks.Many research works have been done for preventing network attacks.Every day,the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly defined.In this regard,this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA(WDLDA).The implemented WDLDA results in a rate of detection accuracy(DRA)of 97%and a false positive rate(FPR)of 11%using the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)dataset.
文摘基于IP(intellectual property)核的系统级芯片的测试已成为SoC(system on chip)发展中的瓶颈,提出了一种采用BBO(biogeography based optimization)算法的Wrapper扫描链设计方法,使得Wrapper扫描链均衡化,从而达到IP核测试时间最小化的目的。本算法基于群体智能,通过实施迁徙操作和变异操作,实现Wrapper扫描链均衡化设计。本文以ITC'02 Test bench-marks中的典型IP核为实验对象,实验结果表明本算法相比BFD(best fit decrease)等算法,能够进一步缩短Wrapper扫描链,从而缩短IP核测试时间。
文摘测试问题已成为SoC发展过程中的瓶颈,提出一种新的Wrapper扫描链平衡算法以期缩短IP核测试时间。算法首先计算Wrapper扫描链长度平均值,再结合特定的余量值,计算得到一个取值区间,记该区间为平均值余量;然后将IP核的内部扫描链按其长度降序排列,每次均将最长的内部扫描链添加到某条Wrapper扫描链上,直到该Wrapper扫描链长度在平均值余量所指定的区间内为止。以ITC'02 SoC Test Benchmarks内的所有测试集为对象完成的实验证明本算法能极其有效的通过扫描链平衡设计缩短IP核测试时间。
文摘提出用于均衡Wrapper扫描链的交换优化算法以及用于测试调度的局部最优算法,这两种算法依据测试总线空闲率(IBPTB)指标,可从IP层和系统顶层对系统芯片(SOC)测试时间实现联合优化,进而使SOC的测试时间大大降低.为了验证两种算法及其联合优化性能的有效性和可靠性,对基于ITC’02国际SOC基准电路进行了相关的验证试验.针对p93791基准电路中core6 IP核,交换优化算法能得到比经典BFD(best fit decreasing)算法更均衡的Wrapper扫描链,在最佳情况下最长Wrapper扫描链长度减少2.6%;针对d695基准电路,局部最优算法根据IP核的IBPTB指标,可使相应SOC的测试时间在最优时比经典整数线性规划(ILP)算法减少12.7%.
