Identification of quantitative trait loci(QTLs)controlling yield and yield-related traits in rice was performed in the F_(2) mapping population derived from parental rice genotypes DHMAS and K343.A total of 30 QTLs go...Identification of quantitative trait loci(QTLs)controlling yield and yield-related traits in rice was performed in the F_(2) mapping population derived from parental rice genotypes DHMAS and K343.A total of 30 QTLs governing nine different traits were identified using the composite interval mapping(CIM)method.Four QTLs were mapped for number of tillers per plant on chromosomes 1(2 QTLs),2 and 3;three QTLs for panicle number per plant on chromosomes 1(2 QTLs)and 3;four QTLs for plant height on chromosomes 2,4,5 and 6;one QTL for spikelet density on chromosome 5;four QTLs for spikelet fertility percentage(SFP)on chromosomes 2,3 and 5(2 QTLs);two QTLs for grain length on chromosomes 1 and 8;three QTLs for grain width on chromosomes1,3 and 8;three QTLs for 1000-grain weight(TGW)on chromosomes 1,4 and 8 and six QTLs for yield per plant(YPP)on chromosomes 2(3 QTLs),4,6 and 8.Most of the QTLs were detected on chromosome 2,so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety.Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection(MAS)breeding.Further,the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.展开更多
Network Intrusion Detection Systems(NIDS)are utilized to find hostile network connections.This can be accom-plished by looking at traffic network activity,but it takes a lot of work.The NIDS heavily utilizes approache...Network Intrusion Detection Systems(NIDS)are utilized to find hostile network connections.This can be accom-plished by looking at traffic network activity,but it takes a lot of work.The NIDS heavily utilizes approaches for data extraction and machine learning to find anomalies.In terms of feature selection,NIDS is far more effective.This is accurate since anomaly identification uses a number of time-consuming features.Because of this,the feature selec-tion method influences how long it takes to analyze movement patterns and how clear it is.The goal of the study is to provide NIDS with an attribute selection approach.PSO has been used for that purpose.The Network Intrusion Detection System that is being developed will be able to identify any malicious activity in the network or any unusual behavior in the network,allowing the identification of the illegal activities and safeguarding the enormous amounts of confidential data belonging to the customers from being compromised.In the research,datasets were produced utilising both a network infrastructure and a simulation network.Wireshark is used to gather data packets whereas Cisco Packet Tracer is used to build a network in a simulated environment.Additionally,a physical network consisting of six node MCUs connected to a laptop and a mobile hotspot,has been built and communication packets are being recorded using the Wireshark tool.To train several machine learning models,all the datasets that were gatheredcre-ated datasets from our own studies as well as some common datasets like NSDL and UNSW acquired from Kaggle-were employed.Additionally,PsO,which is an optimization method,has been used with these ML algorithms for feature selection.In the research,KNN,decision trees,and ANN have all been combined with PSO for a specific case study.And it was found demonstrated the classification methods PSO+ANN outperformed PSO+KNN and PSO+DT in this case study.展开更多
基金supported by the Researchers Supporting Project(RSP-2021/298),King Saud University in Riyadh,Saudi Arabia.
文摘Identification of quantitative trait loci(QTLs)controlling yield and yield-related traits in rice was performed in the F_(2) mapping population derived from parental rice genotypes DHMAS and K343.A total of 30 QTLs governing nine different traits were identified using the composite interval mapping(CIM)method.Four QTLs were mapped for number of tillers per plant on chromosomes 1(2 QTLs),2 and 3;three QTLs for panicle number per plant on chromosomes 1(2 QTLs)and 3;four QTLs for plant height on chromosomes 2,4,5 and 6;one QTL for spikelet density on chromosome 5;four QTLs for spikelet fertility percentage(SFP)on chromosomes 2,3 and 5(2 QTLs);two QTLs for grain length on chromosomes 1 and 8;three QTLs for grain width on chromosomes1,3 and 8;three QTLs for 1000-grain weight(TGW)on chromosomes 1,4 and 8 and six QTLs for yield per plant(YPP)on chromosomes 2(3 QTLs),4,6 and 8.Most of the QTLs were detected on chromosome 2,so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety.Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection(MAS)breeding.Further,the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.
文摘Network Intrusion Detection Systems(NIDS)are utilized to find hostile network connections.This can be accom-plished by looking at traffic network activity,but it takes a lot of work.The NIDS heavily utilizes approaches for data extraction and machine learning to find anomalies.In terms of feature selection,NIDS is far more effective.This is accurate since anomaly identification uses a number of time-consuming features.Because of this,the feature selec-tion method influences how long it takes to analyze movement patterns and how clear it is.The goal of the study is to provide NIDS with an attribute selection approach.PSO has been used for that purpose.The Network Intrusion Detection System that is being developed will be able to identify any malicious activity in the network or any unusual behavior in the network,allowing the identification of the illegal activities and safeguarding the enormous amounts of confidential data belonging to the customers from being compromised.In the research,datasets were produced utilising both a network infrastructure and a simulation network.Wireshark is used to gather data packets whereas Cisco Packet Tracer is used to build a network in a simulated environment.Additionally,a physical network consisting of six node MCUs connected to a laptop and a mobile hotspot,has been built and communication packets are being recorded using the Wireshark tool.To train several machine learning models,all the datasets that were gatheredcre-ated datasets from our own studies as well as some common datasets like NSDL and UNSW acquired from Kaggle-were employed.Additionally,PsO,which is an optimization method,has been used with these ML algorithms for feature selection.In the research,KNN,decision trees,and ANN have all been combined with PSO for a specific case study.And it was found demonstrated the classification methods PSO+ANN outperformed PSO+KNN and PSO+DT in this case study.
